DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendments
This action is in response to the submission filed 06 April 2026 for application 18/171,080. Currently claims 1, 2, 3, and 10 are amended. Claims 12-14 are newly added. Claims 1-14 are pending and have been examined.
The claims interpretation on claim 10 has been withdrawn in view of the amendments made and the arguments presented.
The §112(b) rejection of claims 1-11 has been withdrawn in view of the amendments made to claims 1 and 10.
The §112(b) rejection of claims 10 and 11 has been withdrawn in view of the amendments made.
Response to Arguments
Regarding applicant’s arguments, filed 06 April 2026, see page 7, that in the Office Action, a certified English language translation of the foreign application was required in reply to the Office Action. (Office Action at page 2). Patent Center indicates that the certified copy was received Feb. 27, 2023. Therefore, all requirements for establishing foreign priority have been met. No additional certified copy is required in response to the Office Action, and the requirement should be withdrawn.
Examiners response: Applicant’s arguments have been fully considered but they are not persuasive. Examiner respectfully disagrees that the requirement should be withdrawn because although the certified copy of the foreign document was received on 27 February 2023 it does not have the English translation of the foreign application.
Regarding applicant’s arguments, filed 06 April 2026, see pages 9 – 12, regarding the 103 rejection, Applicant specifically argues that none of the cited references disclose a Deterministic Selection based on a Raster Distance. This argument has been fully considered but is moot in view of the new grounds of rejection presented below necessitated by the amendment since this feature was not previously claimed and has not been previously examined.
Priority
Should applicant desire to obtain the benefit of foreign priority under 35 U.S.C. 119(a)-(d) prior to declaration of an interference, a certified English translation of the foreign application must be submitted in reply to this action. 37 CFR 41.154(b) and 41.202(e).
Failure to provide a certified translation may result in no benefit being accorded for the non-English application.
Specification
The disclosure is objected to because of the following informalities: There is an extra space after the periods in the Abstract. Appropriate correction is required.
Claim Objections
Claims 13 and 14 are objected to because of the following informalities: The phrase on line 3 “…relative the first sample …” is awkwardly worded. Appropriate correction is required.
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 1-5 and 8-14 are rejected under 35 U.S.C. 103 as being unpatentable over Ditzel et al (GenRadar: Self-Supervised Probabilistic Camera Synthesis Based on Radar Frequencies, 2021) in view of Shamsolmoali et al (Road Segmentation for Remote Sensing Images using Adversarial Spatial Pyramid Networks, 2020) and further in view of Gardiner (US 8466874 B1).
Regarding claim 1:
Ditzel teaches: A computer-implemented method for predicting future developments of a traffic scene, comprising ([Abstract] Autonomous systems require a continuous and dependable environment perception for navigation and decision-making. This work combines the complementary strengths of both sensor types in a unique self-learning fusion approach for a probabilistic scene reconstruction in adverse surrounding conditions. Then, at inference time, relying exclusively on radio frequencies, the model successively predicts camera constituents in an autoregressive and self-contained process. Page 148998, Column 1, Section II. DATA COLLECTION AND SENSOR SETTINGS] The presented experiments were conducted on a custom dataset comprising roughly 50 000 samples of time synchronized radar and camera images. The collection captures diverse real-world scenery around Ulm Germany, varying in terms of both weather and lighting conditions. It features all kinds of realistic traffic scenarios ranging from clusters of pedestrians over lost-cargo situations and oncoming vehicles to the passing of trams and buses.):
aggregating scene-specific information about a traffic scene ([Page 148998, Column 1, Section II] It features all kinds of realistic traffic scenarios ranging from clusters of pedestrians over lost-cargo situations and oncoming vehicles to the passing of trams and buses);
using a pre-trained encoder network to transform the aggregated scene-specific information into parameters of a multivariate probability distribution of latent features ([Page 149006, Column 1, Section 3] IMPLEMENTATION DETAILS OF THE CATEGORICAL AUTOENCODERS Even though a wide range of autoencoder architectures exist both in theory and code and despite the fact that weights of numerous well-known networks are readily available for download and deployment in frameworks like [54], the specific data used in this project necessitate custom training. Most backbones are typically pre-trained on purified and cleansed benchmarks. [Page 149010, Column 1, Paragraph 1] Optimized to predict the contained 1000 object classes to high accuracy, this model serves as a feature extractor, effectively transforming high-dimensional images into a lower dimensional latent space in which similar input should have a certain proximity. Tapping into its architecture after the last pooling layer allows to summarize its 2048 activations as multivariate Gaussians by fitting mean and covariance to the respective data distribution under consideration. [Page 149013, Column 2, Section 2)] modal-specific encoders. [Page 149023, Column 1, Last Paragraph] These continuous latents were then decoded into image space to fit multivariate Gaussians to the validation dataset as explained in section III-A5);
selecting samples of the multivariate probability distribution of latent features determined by the parameters ([Page 149010, Column 2, Paragraph 3] To obtain a comprehensive notion of the models' versatility, their latent space utilization for a size of K D jCj D 256 is recorded separately for every latent variable over the validation dataset. To yield a reproducible result, the modes of the data-induced PMF, given by equation (23) are accumulated for every input sample and displayed in Figure 29 for both domains);
wherein the selected samples are selected deterministically, such that each selected sample represents a separate region of the multivariate probability distribution of the latent features ([Page 149005, Column 1, Last Paragraph] In fact, it can be considered a variant of the reparameterization trick proposed in [36] which turns sampling of the latents [Page 149005, Column 2, Paragraph 1] into a deterministic function of the encoders logits and some independent additive noise from a predetermined distribution. [Page 149023, Column 1, Last Paragraph] These continuous latents were then decoded into image space to fit multivariate Gaussians to the validation dataset as explained in section III-A5.),
However, Ditzel does not explicitly disclose: and using a pre-trained decoder network to transform each of the selected samples into an output set of a plurality of output sets; and wherein each selected sample is deterministically selected based on a raster distance (i) between selected samples, or (ii) between each selected sample and a mean value of the multivariate probability distribution of the latent features.
Shamsolmoali teaches, in an analogous system: and using a pre-trained decoder network to transform each of the selected samples into an output set of a plurality of output sets ([Page 8, Column 1, Algorithm 1] Dec is the pre-trained decoder which chooses the appropriate pixels from G(z) for image recovery).
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 computer-implemented method for predicting future developments of a traffic scene of Ditzel to incorporate the teachings of Shamsolmoali to use a pre-trained decoder network to transform each of the selected samples into an output set of a plurality of output sets. One would have been motivated to do this modification because doing so would give the benefit of choosing the appropriate pixels from G(z) for image recovery as taught by Shamsolmoali [Page 8, Column 1, Algorithm 1].
Gardiner teaches, in an analogous system: and wherein each selected sample is deterministically selected based on a raster distance (i) between selected samples, or (ii) between each selected sample and a mean value of the multivariate probability distribution of the latent features ([Column 5, Lines 21-28] Referring to FIGS. 4A and 4B, a point primitive rendering technique is illustrated with two different point positions. Each square 102 represents a pixel within a raster structure 100. A circle 104 represents a point primitive, with a dot 106 representing its center position. Each pixel 102 contains four samples 108 used for anti-aliasing. For example, for a simple filter kernel, the color assigned to each sample 108 within a pixel 102 can be averaged to obtain a final pixel color).
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 combined method of Ditzel and Shamsolmoali to incorporate the teachings of Gardiner wherein each selected sample is deterministically selected based on a raster distance (i) between selected samples, or (ii) between each selected sample and a mean value of the multivariate probability distribution of the latent features. One would have been motivated to do this modification because doing so would give the benefit of improving the overall image quality as taught by Gardiner [Column 5, Line 16].
Regarding claim 2:
The combination of Ditzel, Shamsolmoali and Gardiner teaches: The method according to Claim 1 (as shown above).
Ditzel further teaches: further comprising: adapting the raster distance based on a weight of individual selected samples in the multivariate probability distribution of the latent features ([Page 149011, Column 2, Paragraph 2] Figure 14 contrasts this variational entity of the camera with that of the radar models across consecutive validation runs performed after every training epoch. All measures are with regard to a single latent symbol facilitating the association with an actual number of bits required to transmit its state through the network. Figuratively speaking, perplexity measures the amount of randomness in the model and quantifies how well the associated process predicts samples. It calculates the weighted average number of choices each latent variable is offered. [Page 149013, Column 2, Last Paragraph] As camera and radar input dimensions are fixed [Page 149014, Column 1, Paragraph 1] a priori, so is the length N = hxw of both sequences created line by line using raster order for radar. [Page 149023, Column 1, Last Paragraph] These continuous latents were then decoded into image space to fit multivariate Gaussians to the validation dataset as explained in section III-A5).
Regarding claim 3:
The combination of Ditzel, Shamsolmoali and Gardiner teaches: The method according to Claim 1 (as shown above).
Ditzel further teaches: wherein: at least a portion of the selected samples include noise ([Page 149005, Column 1, Last Paragraph] In fact, it can be considered a variant of the reparameterization trick proposed in [36] which turns sampling of the latents [Page 149005, Column 2, Paragraph 1] into a deterministic function of the encoders logits and some independent additive noise from a predetermined distribution),
and the raster distance between the selected samples is equal ([Page 149013, Column 2, Last Paragraph] As camera and radar input dimensions are fixed [Page 149014, Column 1, Paragraph 1] a priori, so is the length N = hxw of both sequences created line by line using raster order for radar).
Regarding claim 4:
The combination of Ditzel, Shamsolmoali and Gardiner teaches: The method according to Claim 1 (as shown above).
Ditzel further teaches: wherein a predetermined number of the samples are selected ([Page 148998, Column 1, Section II, Paragraph 1] The presented experiments were conducted on a custom dataset comprising roughly 50,000 samples of time synchronized radar and camera images. Note: 50,000 corresponds to predetermined).
Regarding claim 5:
The combination of Ditzel, Shamsolmoali and Gardiner teaches: The method according to Claim 1 (as shown above).
Ditzel further teaches: wherein a determination of a number of the samples to be selected and/or the selection of the samples is based on: a time available for generating the plurality of output sets ([Page 148998, Column 1, Section II, Paragraph 1] The presented experiments were conducted on a custom dataset comprising roughly 50,000 samples of time synchronized radar and camera images. [Page 149013, Column 2, Last Paragraph] Concretely, time-synchronized samples of both domains are encoded into their discrete counterparts by means of the pretrained modal-specific encoders, as explained in section III-A, with all of their weights frozen);
a comparison of a totality of the generated plurality of output sets to a probability distribution of the plurality of output sets ([Page , Column 2, Last Paragraph] More precisely, shrinking the sample space to only a few categories KO K comprising the bulk of the probability mass increases both the sample quality and reliability by preventing low-probability outcomes);
a similarity of the selected samples to training data of the pre-trained encoder network and the pre-trained decoder network; and/or if the totality of the generated plurality of output set provides a plurality of different, predetermined results ([Page 149009, Column 1, Paragraph 1] Table 2 to table 5 show the results for both modalities, different vocabulary sizes and sampling methods).
Regarding claim 8:
The combination of Ditzel, Shamsolmoali and Gardiner teaches: The method according to Claim 1 (as shown above).
Ditzel further teaches: further comprising: generating a possible future trajectory for at least one participant in the traffic scene as one of the output sets of the generated plurality of output sets, and identifying different modes for a future development of the traffic scene based on a totality of the generated plurality of output sets ([Page 149039] Figure 49. Typical fail cases that occur during the probabilistic inference phase (red) below the actual camera ground truth (blue). Note: Top row 2nd block for example corresponds to a possible future trajectory for at least one participant in the traffic scene as one of the output sets. All the blocks correspond to a totality of the generated plurality of output sets. Red and blue correspond to the different modes).
Regarding claim 9:
The combination of Ditzel, Shamsolmoali and Gardiner teaches: The method according to Claim 8 (as shown above).
Ditzel further teaches: further comprising: generating probabilities for a prespecified number of the different modes for the future developments of the traffic scene as one of the output sets of the generated plurality of output sets, wherein the totality of the generated plurality of output sets is taken as a basis for a further prediction step and/or planning step ([Page 149023, Column 2, Paragraph 1] Even the models with modest dictionary sizes of K =64 sample the largest-probability category only about 3 of 4 times, with minor differences between modalities. This does not necessarily harm the overall density estimation and camera sequence prediction goal since other categories might be almost equally suitable candidates, exhibiting probabilities similar to the modes of the distributions).
Regarding claim 10:
Claim 10 is substantially similar to claim 1 and therefore is rejected on similar grounds as claim 1.
Regarding claim 11:
The combination of Ditzel, Shamsolmoali and Gardiner teaches: The system according to Claim 10 (as shown above).
Ditzel further teaches: wherein the encoder network and the decoder network are components of a variational autoencoder architecture or a conditional variational autoencoder architecture ([Page 149002, Column 2, Section A] Variational autoencoders [Page 149003, Column 1, Last Paragraph] Categorical variational autoencoders [39] are a special case of variational inference models described in the former section, most often used when a discrete probabilistic selection of features is desired, as in [Page 149003, Column 2, Paragraph 1] the present case. On an abstract level this architecture consists of an encoder and decoder part with a discrete stochastic bottleneck in between).
Regarding claim 12:
The combination of Ditzel, Shamsolmoali and Gardiner teaches: The method according to Claim 1 (as shown above).
However, the system of Ditzel and Shamsolmoali does not explicitly disclose: wherein: a first sample is positioned on the mean value, and each other sample is located the raster distance from the first sample.
Gardiner teaches, in an analogous system: wherein: a first sample is positioned on the mean value, and each other sample is located the raster distance from the first sample ([Column 5, Lines 24-25] with a dot 106 representing its center position. Note: See Figure 4A. Dot on center position corresponds to first sample on mean value. Other black dots around 106 correspond to other sample is located the raster distance from the first sample).
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 combined method of Ditzel and Shamsolmoali to incorporate the teachings of Gardiner wherein: a first sample is positioned on the mean value, and each other sample is located the raster distance from the first sample. One would have been motivated to do this modification because doing so would give the benefit of improving the overall image quality as taught by Gardiner [Column 5, Line 16].
Regarding claim 13:
The combination of Ditzel, Shamsolmoali and Gardiner teaches: The method according to Claim 1 (as shown above).
However, the system of Ditzel and Shamsolmoali does not explicitly disclose: wherein: a first sample is positioned on the mean value, each other sample forms a square centered relative the first sample, and a side of the square has a length corresponding to two times the raster distance.
Gardiner teaches, in an analogous system: wherein: a first sample is positioned on the mean value, each other sample forms a square centered relative the first sample, and a side of the square has a length corresponding to two times the raster distance ([Column 5, Lines 24-25] with a dot 106 representing its center position. Note: See Figure 4A. Dot on center position corresponds to first sample on mean value. Other black dots around 106 correspond to other sample. Squares are also shown in Figure 4A).
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 combined method of Ditzel and Shamsolmoali to incorporate the teachings of Gardiner wherein: a first sample is positioned on the mean value, each other sample forms a square centered relative the first sample, and a side of the square has a length corresponding to two times the raster distance. One would have been motivated to do this modification because doing so would give the benefit of improving the overall image quality as taught by Gardiner [Column 5, Line 16].
Regarding claim 14:
The combination of Ditzel, Shamsolmoali and Gardiner teaches: The method according to Claim 1 (as shown above).
However, the system of Ditzel and Shamsolmoali does not explicitly disclose: wherein: a first sample is positioned on the mean value, each other sample forms a circle centered relative the first sample, and a radius of the circle is equal to the raster distance.
Gardiner teaches, in an analogous system: wherein: a first sample is positioned on the mean value, each other sample forms a circle centered relative the first sample, and a radius of the circle is equal to the raster distance ([Column 5, Lines 24-25] with a dot 106 representing its center position. Note: See Figure 4A. Dot on center position corresponds to first sample on mean value. Other black dots around 106 correspond to other sample. Circle is also shown in Figure 4A).
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 combined method of Ditzel and Shamsolmoali to incorporate the teachings of Gardiner wherein: a first sample is positioned on the mean value, each other sample forms a circle centered relative the first sample, and a radius of the circle is equal to the raster distance. One would have been motivated to do this modification because doing so would give the benefit of improving the overall image quality as taught by Gardiner [Column 5, Line 16].
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Ditzel et al (GenRadar: Self-Supervised Probabilistic Camera Synthesis Based on Radar Frequencies, 2021) in view of Shamsolmoali et al (Road Segmentation for Remote Sensing Images using Adversarial Spatial Pyramid Networks, 2020) and Gardiner (US 8466874 B1) and further in view of Peled et al (Online Predictive Optimization Framework for Stochastic Demand-Responsive Transit Services, 2019).
Regarding claim 6:
The combination of Ditzel, Shamsolmoali, and Gardiner teaches: The method according to Claim 1 (as shown above).
However, the combination of Ditzel, Shamsolmoali, and Gardiner does not explicitly disclose: wherein the scene-specific information is transformed into an expected value vector and a covariance matrix of a multivariate normal distribution of the latent features.
Peled teaches, in an analogous system: wherein the scene-specific information is transformed into an expected value vector and a covariance matrix of a multivariate normal distribution of the latent features ([Page 7, Paragraph 2] While the expected value formulation may be simple to obtain, its solution nonetheless lacks robustness. [Page 21, Section 4.1.1. Paragraph 4] multivariate normal distribution with covariance matrix. [Page 21, Section 4.1.2., Paragraph 1] In this study, we assume that only travel demands are stochastic, while other parameters, such as travel time between nodes, remain constant over time. To construct the copula, we assume for simplicity that the correlation structure of the data is time-invariant. As such, it suffices to compute the covariance matrix of the copula offline once, based on historical data).
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 system of Ditzel, Shamsolmoali, and Gardiner to incorporate the teachings of Peled wherein the scene-specific information is transformed into an expected value vector and a covariance matrix of a multivariate normal distribution of the latent features. One would have been motivated to do this modification because doing so would give the benefit of this computation being efficiently maintained online as new data becomes known, so that the copula retains the updated state of correlation as taught by Peled [Page 21, Section 4.1.2., Paragraph 1].
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Ditzel et al (GenRadar: Self-Supervised Probabilistic Camera Synthesis Based on Radar Frequencies, 2021) in view of Shamsolmoali et al (Road Segmentation for Remote Sensing Images using Adversarial Spatial Pyramid Networks, 2020) and Gardiner (US 8466874 B1) and further in view of Novi et al (An integrated artificial neural network–unscented Kalman filter vehicle sideslip angle estimation based on inertial measurement unit measurements, 2018).
Regarding claim 6:
The combination of Ditzel, Shamsolmoali, and Gardiner teaches: The method according to Claim 1 (as shown above).
However, the combination of Ditzel, Shamsolmoali, and Gardiner does not explicitly disclose: wherein at least one of the following methods is used for selecting the samples: unscented Kalman filter sampling; Gauss-Hermite quadrature Kalman filter sampling; cubature Kalman filter sampling; randomized unscented Kalman filter sampling; and asymmetric or symmetric localized cumulative distribution sampling.
Novi teaches, in an analogous system: wherein at least one of the following methods is used for selecting the samples: unscented Kalman filter sampling; Gauss-Hermite quadrature Kalman filter sampling; cubature Kalman filter sampling; randomized unscented Kalman filter sampling; and asymmetric or symmetric localized cumulative distribution sampling ([Page 1865, Column 2, Paragraph 1] Using the unscented Kalman filter (UKF)).
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 system of Ditzel, Shamsolmoali, and Gardiner to incorporate the teachings of Novi wherein at least one of the following methods is used for selecting the samples: unscented Kalman filter sampling; Gauss-Hermite quadrature Kalman filter sampling; cubature Kalman filter sampling; randomized unscented Kalman filter sampling; and asymmetric or symmetric localized cumulative distribution sampling. One would have been motivated to do this modification because doing so would give the benefit of allows calculation of the statistics of a random variable which is subject to a non-linear transformation as taught by Novi [Page 1865, Column 2, Paragraph 1].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Szlapczynski (A New Method of Ship Routing on Raster Grids, with Turn Penalties and Collision Avoidance, 2006) discloses a method of finding optimal routes on raster planes. The method presented takes advantage of a new algorithm that tends to minimize a number of direction changes within a route, while steering clear of the obstacles. Two different schemes, suitable for restricted area Vessel Traffic Service (VTS) system and collision avoidance system located on the own ship are described. The VTS-oriented scheme supports VTS priority policy that may extend or override international give-way regulations. The own-ship routing scheme in a give-way situation is capable of determining the shortest safe path to the destination point. The method takes into account own ship dynamics. It has linear time and space complexities and therefore is sufficiently fast to perform real-time routing on the raster grids. Both the general method and the algorithm it uses are presented in detail in the paper.
Yuan (A Survey of Traffic Prediction: from Spatio‑Temporal Data to Intelligent Transportation, 2021) discloses a comprehensive survey on traffic prediction, which is from the spatio-temporal data layer to the intelligent transportation application layer. At first, we split the whole research scope into four parts from bottom to up, where the four parts are, respectively, spatio-temporal data, preprocessing, traffic prediction and traffic application. Later, we review existing work on the four parts. First, we summarize traffic data into five types according to their difference on spatial and temporal dimensions. Second, we focus on four significant data preprocessing techniques: map-matching, data cleaning, data storage and data compression. Third, we focus on three kinds of traffic prediction problems (i.e., classification, generation and estimation/ forecasting). In particular, we summarize the challenges and discuss how existing methods address these challenges. Fourth, we list five typical traffic applications. Lastly, we provide emerging research challenges and opportunities. We believe that the survey can help the partitioners to understand existing traffic prediction problems and methods, which can further encourage them to solve their intelligent transportation applications.
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 CHAITANYA RAMESH JAYAKUMAR whose telephone number is (571)272-3369. The examiner can normally be reached Mon-Fri 9am-1pm.
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/C.R.J./Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128
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