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 .
Status of the Claims
Claims 1-7 have been amended. Claims 1-7 are currently pending and have been considered by the Examiner.
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 1-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-5 recite a method, claim 6 recites an apparatus comprising a processor (an apparatus), and claim 7 recites a non-transitory computer-readable recording medium (a product). Each of a method, an apparatus, and a product falls under one of the four statutory categories of patent eligible subject matter.
Claim 1
Step 2A Prong 1: Extracting a first subset from the plurality of subsets representing the task and a second subset from the plurality of subsets representing the task, wherein the second subset excludes the first subset is an observation mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Generating a task vector representing characteristics of the first subset using parameters of a first neural network is a mathematical calculation. Specification paragraphs [0022]-[0023] and [0031], line 5 explain that Equation 1 is a formula for calculating a task vector hnt and that neural network f includes parameters.
Calculating, from the task vector and the second subset, a predicted value of each value included in the second subset using parameters of a second neural network, wherein the predicted value represents characteristics of the plurality of subsets representing the task is a mathematical calculation. Specification paragraphs [0027]-[0028] and [0031], line 5 explain that Equation 2 is a formula for calculating a predicted value zt and that neural network g includes parameters.
Updating learning target parameters including the parameters of the first neural network and the parameters of the second neural network using an error between each value included in the second subset and the predicted value corresponding to said each value included in the second subset, wherein the updating the learning target parameters further comprises iteratively computing a learning target parameter to minimize a value of the error according to a gradient of the learning target parameters to optimize the learning target parameters are mathematical calculations. In specification paragraphs [0031]-[0034], Equations 5-6 disclose updating parameters to minimize an expected test error. Paragraphs [0043]-[0044] disclose calculating a gradient.
Predicts a third subset that is subsequent to the series data is a judgement mental process which can reasonably be performed in the human mind with the aid of pencil and paper. The claim recites an abstract idea.
Step 2A Prong 2: A computer including a memory and processor for executing a learning method amounts to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
Receiving a series data comprising a plurality of subsets representing a task for learning in the task amounts to mere data-gathering, an insignificant pre-solution activity under MPEP 2106.05(g).
Using parameters of a first neural network, the first neural network comprises a first plurality of layers of parameters and is based on a bidirectional long short-term memory (LSTM), and the task vector is based on a first latent layer of the first plurality of layers amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f). A bidirectional LSTM amounts to a mere field of use and technological environment under MPEP 2106.05(h).
Using parameters of a second neural network amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
The second neural network comprises a second plurality of layers of parameters and is based on an LSTM, and the predicted value is based on a second latent layer of the second plurality of layers of the second neural network amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f). An LSTM amounts to a mere field of use and technological environment under MPEP 2106.05(h).
Using a combination of the first neural network and the second neural network as learnt amounts to invoking computers merely as a tool to perform an existing process under MPEP 2106.05(f).
The additional elements as disclosed above, alone or in combination, do not integrate the abstract ideas into a practical application as they are mere insignificant extra solution activities as disclosed in combination with generic computer functions and mere fields of use that are implemented to perform the abstract ideas disclosed above. The claim is directed to an abstract idea.
Step 2B: A computer including a memory and processor for executing a learning method amounts to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
Receiving a series data comprising a plurality of subsets representing a task for learning in the task is analogous to receiving data over a network, which the courts have recognized as a well-understood, routine, conventional activity under MPEP 2106.05(d)(II).
Using parameters of a first neural network, the first neural network comprises a first plurality of layers of parameters and is based on a bidirectional long short-term memory (LSTM), and the task vector is based on a first latent layer of the first plurality of layers amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f). A bidirectional LSTM amounts to a mere field of use and technological environment under MPEP 2106.05(h).
Using parameters of a second neural network amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
The second neural network comprises a second plurality of layers of parameters and is based on an LSTM, and the predicted value is based on a second latent layer of the second plurality of layers of the second neural network amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f). An LSTM amounts to a mere field of use and technological environment under MPEP 2106.05(h).
Using a combination of the first neural network and the second neural network as learnt amounts to invoking computers merely as a tool to perform an existing process under MPEP 2106.05(f).
The additional elements as disclosed above, in combination with the abstract ideas, are not sufficient to amount to significantly more than the abstract ideas as they are well-understood, routine and conventional activities as disclosed in combination with generic computer functions and mere fields of use that are implemented to perform the abstract ideas disclosed above. The claim is not patent eligible.
Claim 2 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated.
Step 2A Prong 2 and Step 2B: The generating includes generating a value of each latent layer at each time of the bidirectional LSTM as the task vector amounts to mere instructions for applying the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 3 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated. The calculating further comprises generating a value of each latent layer of the LSTM at each time as a vector representing characteristics of the second subset is a mathematical calculation. Specification paragraphs [0025]-[0026] disclose calculating a query vector zt in Equation 2.
Calculating the predicted value of each value included in the series data from the task vector and the vector representing the characteristics of the series data is a mathematical calculation. Specification paragraphs [0027]-[0028] disclose calculating the predicted value x̂t+1 based on task vector hnt and query vector zt using Equations 3 and 4.
Step 2A Prong 2 and Step 2B: The claim does not recite any additional elements which, alone or in combination, would integrate the abstract ideas into a practical application or which, in combination with the abstract ideas, would be sufficient to amount to significantly more than the abstract ideas. The claim is not patent eligible.
Claim 4 incorporates the rejection of claim 3.
Step 2A Prong 1: The abstract ideas of claim 3 are incorporated. The calculating further comprises calculating the predicted value of each value included in the series data through the neural network having the attention mechanism is a mathematical calculation. Specification paragraphs [0027]-[0028] disclose calculating a predicted value x̂t+1 based on zt in Equations 3 and 4.
Step 2A Prong 2 and Step 2B: The second neural network includes a neural network having an attention mechanism, and using this neural network amounts to mere instructions for applying the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 5 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated. The updating further comprises calculating the error using an expected test error or a negative log likelihood is a mathematical calculation, and updating the learning target parameters using the calculated error is a mathematical calculation. In specification paragraphs [0032]-[0034], Equations 5 and 6 are formulas for updating learning target parameters.
Step 2A Prong 2 and Step 2B: The claim does not recite any additional elements which, alone or in combination, would integrate the abstract ideas into a practical application or which, in combination with the abstract ideas, would be sufficient to amount to significantly more than the abstract ideas. The claim is not patent eligible.
Claim 6 recites an apparatus which implements the same features as the method of claim 1 and is therefore rejected for at least the same reasons.
Claim 7 recites an non-transitory computer-readable recording medium which implements the same features as the method of claim 1 and is therefore rejected for at least the same reasons.
In Step 2A Prong 2 and Step 2B, a non-transitory computer-readable recording medium having computer-readable instructions stored thereon, which when executed, cause a computer including a memory and a processor to execute operations of a learning method amount to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f). The claims is not patent eligible.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3 and 5-7 are rejected under 35 U.S.C. 103 as being unpatentable over Banerjee et al. (US 20200352461 A1) in view of Sehovac et al. (“Forecasting Building Energy Consumption with Deep Learning: A Sequence to Sequence Approach”). All references were cited in the PTO-892 issued 02/23/2026.
Regarding claim 1, Banerjee teaches: A learning method, executed by a computer including a memory and processor, the method comprising: ([0027], lines 5-11)
receiving a series data comprising a plurality of subsets representing a task for learning in the task; ([0031], [0033], lines 1-10, and [0034], lines 12-17 discloses acquiring an ECG that includes an R-R interval time series and a P wave time series. A series data is the ECG.)
extracting a first subset from the plurality of subsets representing the task and a second subset from the plurality of subsets representing the task, wherein the second subset excludes the first subset; ([0031], [0032], lines 1-7, all of [0033]-[0034] and Figs. 3A-3B and Fig. 4. A first subset is an R-R interval time series that contains 66 samples (33 seconds at 2 Hz). A second subset is a P wave time series that contains 200 samples, a window that lasts 200 milliseconds and ends 33 milliseconds before a reference R peak. Since the P wave time series (200 ms) is shorter than the R-R interval time series (33 sec), the P wave necessarily excludes some of the R-R samples even when a particular P wave lies entirely within an R-R interval.)
generating a task vector representing characteristics of the first subset using parameters of a first neural network, wherein the first neural network comprises a first plurality of layers of parameters and is based on a bidirectional long short-term memory (LSTM), and the task vector is based on a first latent layer of the first plurality of layers; ([0032]-[0033]; [0035], line 16 to “intervals” in line 20 discloses a bidirectional LSTM network which receives R-R intervals time series. A task vector includes a hidden vector sequence ht, and parameters include all weights W and biases b of the bidirectional LSTM network.)
calculating, from the task vector and the second subset, a predicted value of each value included in the second subset using parameters of a second neural network, wherein the predicted value represents characteristics of the plurality of subsets representing the task, and the second neural network comprises a second plurality of layers of parameters and is based on an LSTM, and the predicted value is based on a second latent layer of the second plurality of layers of the second neural network; and ([0035], lines 16-end, [0036], [0038]-[0039]. A predicted value is either “AF” or “Non-AF”. This prediction characterizes each time series data points in the P waves time series. A “second neural network” is LSTM network 110. The calculating is based on the merged output states of the two LSTM networks 108 and 110, and thus calculating the predicted value uses parameters of the second neural network, LSTM 110.)
updating learning target parameters including the parameters of the first neural network and the parameters of the second neural network using an error between [a correct label]
wherein the updating the learning target parameters further comprises iteratively computing a learning target parameter to minimize a value of the error according to a gradient of the learning target parameters to optimize the learning target parameters, and ([0042], from line 16 to the end of the paragraph. A “value of the error” is a cross-entropy loss, and training over 100 epochs using mini-batches indicates “iteratively computing a learning target parameter”.)
a combination of the first neural network and the second neural network as learnt predicts a third subset
However, Banerjee does not explicitly teach: an error between each value included in the second subset and the predicted value corresponding to said each value included in the second subset,
a third subset that is subsequent to the series data.
But Sehovac teaches: an error between each value included in the second subset and the predicted value corresponding to said each value included in the second subset, (Page 111, col. 2, subsection B, lines 1-20 teaches usage series data. Page 112, col. 2, final 5 lines and Page 113, col. 1, lines 1-5 teaches an error is a difference between an actual value and a predicted value. The limitation of “each value included in the second subset” is each actual target value to be predicted, and “the predicted value corresponding to said each value” is a predicted value.)
a third subset that is subsequent to the series data. (Page 111, Section IV, subsection A, lines 16-26 where “the series data” is training data and “a third subset” is testing data.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have applied Sehovac’s error metric to a prediction by Banerjee’s network architecture disclosed by Fig. 4, and to have selected testing data that is subsequent to training data. A motivation for the combination is to train the LSTM to forecast a time series of data, and because randomizing the entire dataset prior to subsetting (splitting into train and test set) is counter intuitive, it would potentially allow the model to see the most recent energy data during training, while being tested on the first most data. (Sehovac, Page 111, Section IV, subsection A, lines 16-26)
Regarding claim 2, the combination of Banerjee and Sehovac teaches:
The learning method according to claim 1,
Banerjee teaches: wherein the generating includes generating a value of each latent layer at each time of the bidirectional LSTM as the task vector. ([0032]-[0033]; [0035], line 16 to “intervals” in line 20 discloses a bidirectional LSTM network which receives R-R intervals time series. A value of a latent layer at each time include a hidden vector sequence ht.)
Regarding claim 3, the combination of Banerjee and Sehovac teaches: The learning method according to claim 1,
Banerjee teaches: wherein the calculating further comprises generating a value of each latent layer of the LSTM at each time as a vector representing characteristics of the second subset, and ([0032], [0034]; [0035], from “LSTM” in line 20 to line 24 discloses an LSTM network 110 which receives P waves time series. A value of a latent layer of the LSTM at each time include a hidden vector sequence ht.)
calculating the predicted value of each value included in the series data from the task vector and the vector representing the characteristics of the series data. ([0036], [0038]-[0039] teaches calculating a predicted value of “AF” or “Non-AF” based on the hidden vector sequences from the bidirectional LSTM and the LSTM.)
Regarding claim 5, the combination of Banerjee and Sehovac teaches: The learning method according to claim 1,
Banerjee teaches: wherein the updating further comprises calculating the error using an expected test error or a negative log likelihood, and updating the learning target parameters using the calculated error. ([0040], lines 16-end, [0042], lines 2-4 below the “var” formula, and [0043] discloses calculating cross entropy loss during training and minimizing the loss by updating the model parameters. The entire dataset was randomly partitioned into training, validation, and testing. The training loss is “an expected test error” because it is the same error one would expect if test inputs were input into the model before training finishes.)
Claim 6 recites a learning apparatus which implements the same features as the learning method of claim 1 and is therefore rejected for at least the same reasons.
Banerjee teaches: A learning apparatus comprising: a memory; and a processor configured to execute: ([0027], lines 5-11)
Regarding claim 7, the combination of Banerjee and Sehovac teaches: a learning method according to claim 1.
Banerjee teaches: A non-transitory computer-readable recording medium having computer-readable instructions stored thereon, which when executed, cause a computer including a memory and a processor to execute operations ([0009], lines 1-6)
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Banerjee et al. (US 20200352461 A1) in view of Sehovac et al. (“Forecasting Building Energy Consumption with Deep Learning: A Sequence to Sequence Approach”) and Sehovac et al. (“Deep Learning for Load Forecasting: Sequence to Sequence Recurrent Neural Networks With Attention”), hereinafter Sehovac II. All references were cited in the PTO-892 issued 02/23/2026.
Regarding claim 4, the combination of Banerjee and Sehovac teaches: The learning method according to claim 3,
However, Banerjee and Sehovac do not explicitly teach: wherein the second neural network includes a neural network having an attention mechanism, and the calculating further comprises calculating the predicted value of each value included in the series data through the neural network having the attention mechanism.
But Sehovac II teaches: wherein the second neural network includes a neural network having an attention mechanism, and (Page 3, col. 1, section C, line 1 to col. 2, equation (10); and Page 5, col. 2, section C, lines 1-2)
the calculating further comprises calculating the predicted value of each value included in the series data through the neural network having the attention mechanism. (Page 9, col. 1, equation (26) and lines 1-2 below equation (27).)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have incorporated Sehovac II’s attention mechanism into the neural network of Banerjee and Sehovac. A motivation for the combination is that the attention mechanism alleviates the burden of connecting encoder and decoder. (Sehovac II’s Abstract, lines 10-11)
Response to Arguments
Below are the Examiner’s responses to the Applicant’s arguments filed on 05/22/2026.
Applicant’s Arguments Under 35 U.S.C. 101 (Pages 8-10): On page 9, the arguments summarize the Office’s memo on Desjardins and the Enfish court, and issues addressed by the Applicant’s specification.
Applicant respectfully submits that it is impractical for the human mind to perform the operations according to the logical structures and processes as detailed in the limitations [of claim 1]. The limitations are not insignificant extra-solution activities because these limitations are integral part of the machine learning using series data as training data. The limitations improve a technical field of training the neural network that, after being trained, predicts series data of a task with accuracy. The limitation positively recites a technical effect of the updating parameters of the respective neural networks, as Specification supports in paragraphs [0044] and [0049]. Thus, claim 1 as a whole describes improvement of logical structures and processes in sufficient details as improvement to software, which describes non-abstract improvements to computer functionality.
Examiner’s Response: Applicant's arguments have been fully considered but they are not persuasive. It appears that the arguments cite the Office’s memo on Desjardins and the Enfish court because the method of claim 1 could be construed as a type of software, executed by a computer including a memory and processor, for training neural networks. Instant claim 1 as a whole is different from the claims in Desjardin and Enfish. Examiner considers the combination of features for each claim when performing an inquiry under 35 U.S.C. 101. In Step 2A Prong 2 and Step 2B, a memory and processor amount to nothing more than generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f). Generic computer components are not sufficient to incorporate the abstract ideas into a practical application or provide a technical improvement.
In response to the argument that it is impractical for the human mind to perform the limitations of claim 1, the limitation “extracting a first subset from the plurality of subsets representing the task and a second subset from the plurality of subsets representing the task, wherein the second subset excludes the first subset” is an observation mental process, and the limitation “predicts a third subset that is subsequent to the series data” is a judgement mental process which can reasonably be performed in the human mind with the aid of pencil and paper. A person could practically partition task data into a plurality of data subsets in his or her mind, and a person could practically predict future time series data based on given time series data in his or her mind. The arguments fail to explain why these steps are allegedly impractical to perform in the human mind.
Examiner respectfully disagrees with the argument that the limitations improve a technical field of training the neural network that, after being trained, predicts series data of a task with accuracy. In Step 2A Prong 1, the limitations describing updating learning target parameters (claim 1, from line 21 to “parameters” on page 4, line 1) are mathematical calculations, which are a type of judicial exception. In specification paragraphs [0031]-[0034], Equations 5-6 disclose updating parameters to minimize an expected test error. Paragraphs [0043]-[0044] disclose calculating a gradient. The limitation of predicting a third subset that is subsequent to the series data is a judgement mental process. MPEP 2106.05(a) states, “It is important to note, the judicial exception alone cannot provide the improvement.” MPEP 2106.05(a), subsection II states, “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology.”
In Step 2A Prong 2, using a combination of the first neural network and the second neural network as learnt amounts to invoking computers merely as a tool to perform an existing process under MPEP 2106.05(f).
Examiner respectfully disagrees with the argument “the limitations are not insignificant extra-solution activities because these limitations are integral part of the machine learning using series data as training data.” The claim 1 limitation “receiving a series data comprising a plurality of subsets representing a task for learning in the task” amounts to mere data-gathering, which is an insignificant pre-solution activity under MPEP 2106.05(g). The language “for learning in the task” is an intended use for the series data. The claim positively recites updating parameters based on the subsets starting at line 21, which are mathematical calculations, but an act of receiving learning data is not sufficient to incorporate the update calculations into a practical application or provide a technical improvement.
Applicant’s First Arguments Under 35 U.S.C. 103 (Pages 10-13): Page 12 states, “Accordingly, Banerjee focuses on a temporal analysis of R-R intervals and P wave regions by merging the features of the R-R intervals and the P wave regions. Banerjee does not and does not need to describe extracting the second subset from series data by excluding the first subset from the second subset. Nor does Banerjee describe predicting, from the task vector and the second subset, values of the second subset for updating parameters of the respective neural networks based on error between the predicted values and values of the second subset.”
Page 12 states, “If one skilled in the art were to modify the ECG analysis of Banerjee to combine with the teachings of Sehovac, the result would still describe merging features of two regions of ECG while minimizing loss during training. The result would not and would not need to describe extracting the second subset that excludes the first subset from series data of a task. Not would the result need to describe calculating predicted values of a second subset of the given series data using a second neural network.”
Examiner’s Response: Applicant's arguments have been fully considered but they are not persuasive. Banerjee describes extracting the second subset from series data by excluding the first subset from the second subset at least at [0032], lines 1-7 and [0033]-[0034]. A first subset is an R-R interval time series that contains 66 samples (33 seconds at 2 Hz). A second subset is a P wave time series that contains 200 samples, a window that lasts 200 milliseconds and ends 33 milliseconds before a reference R peak. Since the P wave time series (200 ms) is shorter than the R-R interval time series (33 sec), the P wave necessarily excludes some of the R-R samples even when a particular P wave lies entirely within an R-R interval.
Banerjee teaches “generating a task vector representing characteristics of the first subset using parameters of a first neural network” at [0032]-[0033]; [0035], line 16 to “intervals” in line 20. Banerjee teaches the calculating limitation recited in lines 15-20 at [0035], lines 16-end, [0036], [0038]-[0039], where a predicted value is either “AF” or “Non-AF”. The arguments do not explain why Banerjee allegedly fails to teach the generating and calculating limitations. These argument amount to mere allegation of patentability.
Banerjee teaches “updating learning target parameters including the parameters of the first neural network and the parameters of the second neural network using an error between [a correct label] and the predicted value corresponding to said [label],” at [0040], lines 7-12 and 16-end.
Banerjee teaches “wherein the updating the learning target parameters further comprises iteratively computing a learning target parameter to minimize a value of the error according to a gradient of the learning target parameters to optimize the learning target parameters” at [0042], from line 16 to the end of the paragraph. A “value of the error” is a cross-entropy loss, and training over 100 epochs using mini-batches indicates “iteratively computing a learning target parameter”.
However, Banerjee does not explicitly teach: an error between each value included in the second subset and the predicted value corresponding to said each value included in the second subset,
But Sehovac teaches this limitation at page 111, col. 2, subsection B, lines 1-20; Page 112, col. 2, final 5 lines and Page 113, col. 1, lines 1-5. An error is a difference between an actual value and a predicted value. The limitation of “each value included in the second subset” is each actual target value to be predicted, and “the predicted value corresponding to said each value” is a predicted value.
It is noted that the limitations “calculating… a predicted value of each value included in the second subset” (lines 15-16) and “an error between each value included in the second subset and the predicted value corresponding to said each value included in the second subset” (lines 22-24) are recited in a broad manner without specifically disclosing the actual calculation or how the different values utilized to calculate the prediction. The claim merely requires that each value included in the second subset is a sample of series data representing a task. Banerjee teaches calculating prediction errors between a predicted classification (AF or non-AF) and an actual classification, and Sehovac teaches calculating errors between predicted time series data and actual time series data. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date to have applied Sehovac’s error metric to a prediction by Banerjee’s network architecture disclosed by Fig. 4, with a motivation to train the LSTM to forecast a time series of data.
Applicant’s Second Arguments Under 35 U.S.C. 103 (Page 13): Claim 3 was rejected under 35 U.S.C. § 103 as allegedly obvious over Banerjee in view of Sehovac and further in view of Sehovac II… If one skilled in the art were to modify the ECG analysis of Banerjee to combine with the teachings of Sehovac, the result would still describe merging features of two regions of ECG while minimizing loss during training. The result would not and would not need to describe extracting the second subset that excludes the first subset from series data of a task. Not would the result need to describe calculating predicted values of a second subset of the given series data using a second neural network.
Examiner’s Response: Claim 3 was rejected as being obvious over Banerjee in view of Sehovac, whereas claim 4 was rejected as being obvious over Banerjee in view of Sehovac and Sehovac II. The Examiner’s response to the Applicant’s first argument under 103 explains how Banerjee alone teaches limitations of extracting the second subset that excludes the first subset from series data of a task, and calculating predicted values of a second subset of the given series data using a second neural network.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 Asher H. Jablon whose telephone number is (571)270-7648. The examiner can normally be reached Monday - Friday, 9:00 am - 6:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abdullah Al Kawsar can be reached at (571)270-3169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/A.H.J./Examiner, Art Unit 2127
/ABDULLAH AL KAWSAR/Supervisory Patent Examiner, Art Unit 2127
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