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 .
Claims 1-6, 8-10, and 12 are presented for examination.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Rejections - 35 USC § 103
Claims 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Anisimov et al. (US 20200364539) (“Anisimov”) in view of Davies et al. (US 20200003659) (“Davies”) and further in view of Glugla (US 20180320611) (“Glugla”) and Hackworth et al. (US 20090242197) (“Hackworth”).
Regarding claim 9, Anisimov discloses “[a] method for training a deep neural network to determine change-point times in a sensor signal, the method comprising:
providing a plurality of training datasets, each training dataset including an evaluation signal time series (separate normalization of each word from different sources may provide average values for a gaze tracks feature for each normalized word over all occurrences in the training data set; for example, the sum of the gaze tracks features of all n-occurrences of the word “water” are averaged over the number of occurrences n – Anisimov, paragraph 195; see also paragraph 221 (describing the dataset gathered as a multidimensional time series; note that the dataset may be divided into subsets to form a plurality)) …, each evaluation signal time series corresponding to a respective evaluation time window of a sensor signal time series from a sensor (data recorded by an EEG may be considered as a sequence of overlapping time interval windows, wherein the length of each window is similar to each other, and the time shift between the windows may be chosen from 0 ms to window length/2; each window consists of fragments of a multichannel EEG time series [evaluation signal time series = combination of data from two or more windows of time; sensor signal extracts = single windows of the time series] –Anisimov, paragraph 223; see also paragraph 90 (disclosing the use of sensors to collect the raw data)) …;
determining sensor signal extracts from each evaluation signal time series of the plurality of training datasets, the sensor signal extracts being time-shifted with respect to one another or respectively offset from one another by a number of sensing steps, the sensor signal extracts being shorter in length than the evaluation signal time series (data recorded by an EEG may be considered as a sequence of overlapping time interval windows, wherein the length of each window is similar to each other, and the time shift between the windows may be chosen from 0 ms to window length/2; each window consists of fragments of a multichannel EEG time series [evaluation signal time series = combination of data from two or more windows of time; sensor signal extracts = single windows of the time series, which are shorter in length than the evaluation signal time series and are offset from each other by up to window length/2] – Anisimov, paragraph 223);
determining one or more frequency contributions from the sensor signal extracts of each evaluation signal time series using a fast Fourier transform (“FFT”) or a Goertzel algorithm (a feature extraction method based on FFT is applied to each window [sensor signal extract], generating a tensor of extracted features for each considered window of the EEG signal – Anisimov, paragraph 223; see also paragraph 132 (indicating that the features may include frequency domain features such as power)); and
training the deep neural network … using the one or more frequency contributions (tensors produced by FFT [frequency contributions] are used as input for [i.e., evaluated by] a convolutional neural network of the training machine learning model [deep learning model] – Anisimov, paragraph 223; see also paragraph 18 (disclosing training of the models)) ….”
Anisimov appears not to disclose explicitly the further limitations of the claim. However, Davies discloses “providing training datasets … including a change-point time (method of training a machine-learning classifier comprises, inter alia, extracting an event section of the input signal comprising a section of the signal from a point prior to a change to a point after the change, extracting pre- and post-event portions of the event section and transforming them into the frequency domain, determining a probability that an appliance is in a degradation state by applying the machine learning classifier to a feature vector comprising the frequency-domain data, and training the machine learning classifier using the annotated feature vectors [i.e., the training dataset comprises feature vectors derived from change-point times] – Davies, paragraphs 339-48); … [and]
training the … model to determine change-point times in the evaluation signal time series using the … change-point times associated therewith (method of training a machine-learning classifier comprises, inter alia, extracting an event section of the input signal comprising a section of the signal from a point prior to a change to a point after the change, extracting pre- and post-event portions of the event section and transforming them into the frequency domain, determining a probability that an appliance is in a degradation state by applying the machine learning classifier to a feature vector comprising the frequency-domain data, and training the machine learning classifier using the annotated feature vectors [i.e., the training dataset comprises feature vectors derived from change-point times and is used to determine change-point times] – Davies, paragraphs 339-48).” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Anisimov to train the model using change-point times, as disclosed by Davies, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would allow the system to pinpoint change times automatically, thereby allowing the user to make necessary corrections to the system being evaluated. See Davies, paragraphs 4 and 339-48.
Neither Anisimov nor Davies appears to disclose explicitly the further limitations of the claim. However, Glugla discloses that the “sensor [is] arranged within … a fuel injection system of an internal combustion engine (amount of exhaust gas recirculation (EGR) entering the intake manifold may be determined based on an EGR flow rate as measured with an EGR differential pressure sensor – Glugla, paragraph 72; fuel injection amount is calculated based on the cylinder air amount calculated from the EGR amount – id. at paragraphs 74-75 [i.e., the sensor is part of the injector system]) ….” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Anisimov and Davies to perform the method using sensor data from a fuel injector, as disclosed by Glugla, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would improve fuel economy and extend the life of engine components. See Glugla, paragraph 10.
Hackworth discloses “a sensor arranged within an injector valve (sensors may be located inside an injection valve without the addition of a separate sensor carrier – Hackworth, paragraph 56) ….” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Anisimov, Davies, and Glugla to arrange the sensor inside the injection valve, as disclosed by Hackworth, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would obviate the need for more complex systems to communicate with the sensor. See Hackworth, paragraph 56.
Regarding claim 10, the rejection of claim 9 is incorporated. Anisimov further discloses a “deep neural network (invention relates to determining the correlation of the eye-movements and/or facial expressions of a user who is consuming visual information; the determination of the correlation may include training one or more machine learning models that include deep neural networks within their architecture – Anisimov, paragraph 18) ….”
Anisimov/Glugla/Hackworth appears not to disclose explicitly the further limitations of the claim. However, Davies discloses that “the … network … is trained using a backpropagation-based training method (training process may involve, inter alia, back-propagating the error and updating the neural network parameters accordingly – Davies, paragraphs 241-45).” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Anisimov/Glugla/Hackworth to train the model using backpropagation, as disclosed by Davies, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would increase the accuracy of the network by ensuring that it has the correct weights. See Davies, paragraph 253.
Allowable Subject Matter
Claims 1-6, 8, and 12 are allowed.
The following is an examiner’s statement of reasons for allowance: None of the prior art of record appears to disclose explicitly at least the following limitations of independent claim 1 as amended, when considered in combination with the remainder of the independent claims:
determining a change-point time within the evaluation time window using a trained data-based sensor model, the trained data-based sensor model being configured to determine the change-point time based on the one or more frequency contributions; [and]
monitoring a quantity of fuel that has been injected by (i) determining opening times and closing times of the injector valve based on the change-point time and (ii) determining opening durations based on the opening times and the closing times ….
The closest prior art of record is Glugla, which discloses monitoring an injected quantity of fuel in general, but does not disclose that this monitoring is performed by determining opening and closing times of the valve based on the change-point time and by determining opening durations based on the opening and closing times. The other references of record also do not disclose this element of the claim.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Response to Arguments
Applicant's arguments filed July 15, 2026 (“Remarks”) have been fully considered but they are, except insofar as rendered moot by the withdrawal of a ground of rejection, not persuasive.
Applicant argues that Anisimov/Davies/Glugla/Hackworth fail to teach all the limitations of claim 9 because they do not teach evaluation signal time series corresponding to evaluation time windows of sensor time series from sensors arranged within an injector valve of an internal combustion engine and training the network to determine change-point times in the evaluation signal time series using frequency contributions and change-point times. Remarks at 14.
In making this argument, Applicant refers the reader to allegedly similar arguments given with respect to the rejection of claim 1 and does not make further arguments with respect to claim 9. However, it is unclear what the similarity between the claim 1 arguments and the claim 9 arguments might be, given that most of the arguments with respect to claim 1 are to the effect that the cited art does not teach claim 1 as amended, and claim 9 has not been similarly amended. As far as Examiner can discern, the only claim 1 argument that Applicant presents that would be equally applicable to claim 9 is that combining Glugla with Hackworth would not arrive at a sensor arranged within an injector valve of a fuel injection system because the injector of Hackworth is for injecting water into a well system rather than for injecting fuel into an engine. Remarks at 8-9. However, Hacworth is relied upon only to teach situating the sensor within the injector valve. Since Glugla teaches an injector valve of a fuel injection system, it would require no inventive effort to use the teachings of Hackworth similarly to situate the sensor of Glugla inside the injector valve.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RYAN C VAUGHN whose telephone number is (571)272-4849. The examiner can normally be reached M-R 7:00a-5:00p ET.
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/RYAN C VAUGHN/ Primary Examiner, Art Unit 2125
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