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 .
Claim Status
Claims 1, 4-7, 9-11, 14-15, and 17-20 are currently pending and under examination herein.
Claims 2-3, 8, 12-13, and 16 are canceled.
Claims 1, 11, and 19 are independent claims.
Withdrawn Rejections/Objections
Rejections and/or objections not reiterated from previous office actions are hereby
withdrawn in view of the amendments filed 12/01/2025.
The 35 U.S.C. 101 rejection of claims 1-7, 9-15, and 17-20 in the office action filed 04/03/2026 is withdrawn in view of amendments filed 07/03/2026.
The 101 rejections are withdrawn at least in view of the analysis Step 2A, 2nd prong, 1st consideration relating to an improvement to other technology integrating possible judicial exceptions into a practical application (MPEP 2106.04(d) and (d)(1)), the improvement to other technology and or technical field applied to the field of MD simulation analysis and visualization, in this instance comprising reduced noise and increased sensitivity through data filtering and reduction in missing rare molecular dynamics states. Applicant's 7/3/2026 remarks at pp. 11-14 further support withdrawal of the rejection.
The following rejections and/or objections are either maintained or newly applied. They constitute the complete set presently being applied to the instant application.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 4-6, 9, 11, 14, 17, and 19-20
Response to Applicant’s Arguments
Applicant's arguments filed 07/30/2026 have been considered but they are not yet persuasive.
Applicant states (pg. 16);
Nakamura, Harada, Likid, and Xu does not teach, suggest, or render obvious at least, for example, the features of "separating, using a data separation module, targeted state data from the current layer of data using the targeted state ... identifying, from the abnormal data, untargeted data that is not statistically relevant to the targeted state data in a current iteration ... reusing the untargeted data as input for a next iteration, wherein next layers of the plurality of layers are determined iteratively based on the untargeted data reused for the next iteration"
Nakamura teaches generating MD trajectories according to MD and storing them (for example, current layer) (Figure 1 and 6). Nakamura further teaches extracting trajectories by the OFLOOD unit (for example, detecting, separating, and extracting a targeted state using abnormal data) (Figure 1 and 3). Nakamura further teaches clustering trajectories by FlexDice during OFLOOD (for example separating targeted state from current layer) (Figure 1 and 7; [0062] [0075]). Nakamura further discloses identifying untargeted data/ outlier as the result of FlexDice clustering [0079]. Nakamura further teaches that OFLOOD unit performs a MD simulation with the detected outlier as the initial structure to generate a trajectory (for example, reusing untargeted data in the next iteration) ([0088] Figure 1).
Additionally, Harada discloses initial structures to be resampled, multiple MD simulations are restarted from these structures, generating a set of transition pathways (determining current layer of data). Harada further teaches that the conformational resampling from states with lower probabilities promote conformational transitions toward neighboring metastable states (ranking based on probabilities).
Using anomaly states to spawn the next cycle of parallel simulation to breakaway from baseline state to discover new target states (for example, separating targeted state data from the current layer data) (pg: 6719, col. 1, last two para.- col. 2, first para.; Figure 1a, 1b).
Harada further discloses identifying rarely occurring, but essential states of proteins based on the degrees of an anomaly. In more detail, ad-PaCS-MD adopts an algorithm called an anomaly detection generative adversarial network (anoGAN) to define an anomaly score for detecting rarely occurring configurations (pg. 6717, col. 2, para. 3).
Harada discloses using a machine learning model (anoGAN) to detect rarely accruing states/ anomaly data. Harada further teaches that the selected configurations are intensively resampled by MD simulation and finally PaCS-MD repeats the cycle of anomaly detection and conformational resampling (pg: 6719, col. 1, last two para.- col. 2, first para.; Figure 1a, 1b: The workflow of ad-PaCS-MD. ad-PaCS-MD repeats the following cycle consisting of (1) the detection of protein configurations with higher anomaly scores (A) and (2) the conformational resampling from them. When a conformational search has sufficiently converged, ad-PaCS-MD is terminated).
Harada teaches that rarely occurring states of proteins are structurally or dynamically
identified based on the predefined measures (pg. 6719, col. 1, para. 1).
Claims 1, 4-6, 9, 11, 14, 17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Nakamura et al. (PUB No. US20170039268A1; as cited in 892 form dated 07/18/2024)), in view of Harada et al. (Enhanced Conformational Sampling Method Based on Anomaly Detection Parallel Cascade Selection Molecular Dynamics: ad-PaCS MD, Journal of Chemical Theory and Computation, September 14, 2020, 16,6716−6725; as cited in 892 form dated 04/03/2026), in view of Likić et al. (A statistical approach to the interpretation of molecular dynamics simulations of calmodulin equilibrium dynamics, Protein Sci. 2005 Dec; 14(12): 2955–2963; as cited in 892 form dated 07/18/2024), and further in view of Xu et al. (US20140066321A1 as cited in 892 form dated 04/03/2026).
Regarding claims 1, 11, and 19:
“A method of finding an unknown molecular dynamics state comprising: receiving input molecular dynamics simulation data;”
Nakamura discloses that the storage unit 11 stores a plurality of structures of substances whose structure changes (substance structures 11a-1, 11a-2,...) and a dimension group 11b, which is a dimension group representing a plurality of dimensions of the structure of the substance as an index for structural analysis (i.e. disclosing molecular dynamic simulation data). Using OFLOOD, time series data of atomic coordinates or "trajectories" produced by MD simulations are clustered ([0036] FIG. 1).
Nakamura et al. discloses the storage that stores a plurality of structures of a substance/index dimensions (molecular dynamics simulation data) (p. 0036) that are atomic coordinated or trajectories which are generated by MD simulation and are to be used for clustering and further MD simulations (p. 0006). Also, Nakamura et al. discloses an index dimension extracting method that performs extraction for each of candidate dimensions, which are not included in a dimension group which is a group of index dimensions that are indices for structural analysis of a substance whose structure changes, out of a plurality of dimensions that express a structure of the substance (molecular dynamic simulation data) (page 7, claim 5).
“Determining a current layer of data from the input molecular dynamics simulation data;”
Nakamura et al. discloses out of the plurality of dimensions that express the structure of a substance; the computing unit sets (separates) a plurality of dimensions that are not included in the dimension group (current layer of data) as candidate dimensions, for example d3, d4, and d5 (abnormal data). (p. 0037). Nakamura further discloses program and an application program executed by the processor [0047].
Nakamura et al. further explains that in the d3, d4 and d5 dimensions are candidates for the hidden dimension (current layer of data) [0085].
“Detecting, abnormal data”
Nakamura discloses that the computing unit 12 performs analysis according to OFLOOD (outlier FLOODing: iterating outlier detection and resampling). When executing OFLOOD, the computing unit 12 carries out clustering for a plurality of structures in a multidimensional space that has the index dimensions included in the dimension group 11 b as coordinate axes and performs a MD simulation with an outlier structure that is not included in any of the clusters as the initial structure. Nakamura discloses that when the metastable structures are set as C1 and C2, clusters are generated for each metastable structure ([0084], FIG. 9)
“Separating, using an anomaly detection module, abnormal data from the current layer of data;”
Nakamura et al. discloses out of the plurality of dimensions that express the structure of a substance; the computing unit sets (separates) a plurality of dimensions that are not included in the dimension group (current layer of data) as candidate dimensions, for example d3, d4, and d5 (abnormal data). (p. 0037). Nakamura further discloses program and an application program executed by the processor [0047]. Nakamura further discloses clustering in a three-dimensional space having dimensions "dl, d2, and d3", performs clustering in a three-dimensional space having dimensions "dl, d2, and d4", and performs clustering in a three-dimensional space having dimensions "dl, d2, and d5" [0085].
“Extracting, using a state detection module, a targeted state using the abnormal data;
Nakamura et al. discloses that for each dimension in the plurality of candidate dimensions (abnormal data), the computing unit carries out clustering of substance structures that has all or the index dimensions included in the dimension group (normal data) and also the candidate dimension (abnormal data) as coordinate axes. (p. 0037). Then the computing unit determines a hidden dimension (targeted state) (p. 0038). Therefore, using abnormal data to extract a targeted state by means of clustering. Nakamura further discloses program and an application program executed by the processor [0047].
and separating, using a data separation module, targeted state data from the current layer of data using the targeted state.”
Nakamura et al. discloses that after determining the hidden dimension (targeted state) by the computing unit, it adds the hidden dimension (targeted state) to the dimension group 11 b (step S2) (p. 0038). Figure 1, also confirms the separation of hidden dimension d3 (targeted state) in step S2. Nakamura further discloses program and an application program executed by the processor [0047]. Furthermore, Nakamura discloses clustering by FlexDice to find regularities from high dimensional data…during clustering according to FlexDice the targeted state , clusters 41 and 42, are separated from the current layer/MD trajectories [0075-0079].
“identifying, from the abnormal data, untargeted data that is not statistically relevant to the targeted state data in a current iteration; reusing the untargeted data as input for a next iteration, wherein next layers of the plurality of layers are determined iteratively based on the untargeted data reused for the next iteration”
Nakamura discloses that when executing OFLOOD, the computing unit 12 carries out clustering for a plurality of structures in a multidimensional space that has the index dimensions included in the dimension group 11 b as coordinate axes and performs a MD simulation with an outlier structure that is not included in any of the clusters as the initial structure [0040]. Nakamura further disclose that when FlexDice is executed, a protein structure not included in any cluster is extracted as an outlier. The OFLOOD unit performs a MD simulation with the detected outlier as the initial structure to generate a trajectory. By adding a hidden dimension and executing FlexDice it is possible, to accurately find a cluster (metastable structure) This means that the detection accuracy for outliers that are not included in clusters (i.e., are not metastable structures) also increases [0088-0089].
“Storing the identified untargeted data, based on a user-defined threshold”
Nakamura further discloses that when a MD simulation has been performed, the computing unit 12 stores the structure of the substance generated by the MD simulation in the storage unit 11. The computing unit 12 then repeatedly executes the processing in steps S1 to 33 above every time the structure of a substance is stored it the storage unit 11 [0040].
“And identifying a drug target based on the targeted state data” and outputting a visualization of the targeted state and targeted state data, wherein the extraction of the targeted state further comprises performing a first clustering to find targeted samples among abnormal samples separated from the current layer of data, wherein the target samples exemplify the targeted state and the abnormal sample correspond to abnormal sample.
Nakamura et al. discloses using the present embodiments in drug design (p. 0106). Nakamura further discloses that the computing unit carries out clustering of substance structures that has all or the index dimensions included in the dimension group (normal data) and also the candidate dimension (abnormal data) as coordinate axes. (p. 0037). Then the computing unit determines a hidden dimension (targeted state) (p. 0038). Therefore, using abnormal data to extract a targeted state by means of clustering. Nakamura further discloses that FlexDice during OFLOOD visualizes the changes in the distribution of clusters (i.e., metastable structures). The OFLOOD results are visualized for example by displaying, on the monitor [0062].
Further regarding claim 19:
“A system configured to perform an iterative method of finding unknown molecular dynamics states and corresponding samples, the system comprising: a communication interface configured to receive molecular dynamics data, the molecular dynamics data simulating movement of particles;”
Nakamura et al. discloses a connecting interface, which is a communication interface that allows peripheral devices such as memory device to be connected to the computer (“the system”) (p. 0053). And that the memory is configured to store structures of a substance (simulation data) whose structure changes (movement of particles) and a dimension group (p. 0020).
“A processor configured to determine a current layer of data from the molecular dynamics data, separate abnormal data from the current layer of data, extract a targeted state using the abnormal data, and separate targeted state data from the current layer of data using the targeted state extracted using the abnormal data;”
Nakamura et al. discloses a processor configured to perform a procedure including: performing, for each of a plurality of candidate dimensions, which are not included in the dimension group, out of the plurality of dimensions, clustering of the plurality of structures in a multidimensional space that has every index dimension included in the dimension group and a candidate dimension as coordinate axes; and adding a specified candidate dimension for which it is possible to generate, a largest number of clusters to the dimension group as an index dimension. (p. 0020).
“And a memory configured to store the targeted state and its data derived from the molecular dynamics data.”
Nakamura et al. discloses a memory configured to store a plurality of structures of a substance whose structure changes and a dimension group, which is a group of index dimensions that are indices for structural analysis of the substance, out of a plurality of dimensions that express a structure of the substance (p.0020). Figure. 1 and 3 disclose the storage unit 11 that stores structure of the substance/significant dimension information (simulation data) and hidden dimension information (targeted state).
Further regarding claim 11:
“A non-transitory computer readable medium
comprising computer executable instructions which when executed by a computer system cause the computer to perform the method for finding an unknown molecular dynamics state comprising”
Nakamura et al. discloses a non-transitory computer-readable storage medium storing a computer program, the computer program that causes a computer to perform a procedure comprising: performing, for each of a plurality of candidate dimensions, which are not included in a dimension group which is a group of index dimensions that are indices for structural analysis of a substance whose structure changes, out of a plurality of dimensions that express a structure of the substance, clustering of a plurality of structures of the substance in a multidimensional space that has every index dimension included in the dimension group and a candidate dimension as coordinate axes; and adding a specified candidate dimension for which it is possible to generate a largest number of clusters to the dimension group as an index dimension (page 7, claim 6).
Further regarding claims 1, 11, and 19:
Nakamura discloses storing, in the memory, structure of the substance generated during the structural analysis and repeatedly executing the clustering, the adding, and the structural analysis every time the structure is stored in the memory (claim 3 [0040]).
Harada discloses a rare-event sampling method called anomaly detection parallel cascade selection molecular dynamics (ad-PaCS-MD) using Machine learning. Harada further discloses rarely occurring but essential states (configurations) of proteins for the transitions are identified based on the degrees of an anomaly. In more detail, ad-PaCS-MD adopts an algorithm called an anomaly detection generative adversarial network (anoGAN) as a measure for detecting rarely occurring states to be resampled. Here, the essential configurations with higher degrees of the anomaly are selected with anoGAN and intensively resampled by restarting short-time MD simulations from the selected configurations. By repeating the detections, ranking, and resampling of configurations with the higher degrees of the anomaly, ad-PaCS-MD automatically and efficiently promotes the rare events (abstract). Harada further discloses mapping noise in latent space, defining a norm, first, a set of normal data of a given protein (reactant) is prepared, specifying a distance matrix, normal sampling by generating by learning the normal distance matrices. (see also, section: 2.2. ad-PaCS-MD, pg. 6719; Figure 1).
With regards to limitations of detecting abnormal data by calculating and ranking an absolute value of probabilistic z-scores of latent variables and ranking the probabilistic z-scores Nakamura discloses detecting abnormal data (when the metastable structures are set as C1 and C2, clusters are generated for each metastable structure ([0084], FIG. 9)).
Likić et al. analyses large amount of data generated by MD simulation using statistical methods to extract meaningful information about protein structure. Likić discloses use of analogs of z-score that uses median absolute deviation to detect outliers in Molecular Dynamic simulation data (p. 2961, col. 2, para. 1). Additionally, Xu discloses performing Z-testing, i.e., calculating an absolute Z score (see Example 1). Z-testing is typically utilized to identify significant differences between a sample mean and a population mean… a Z-score with an absolute value greater than 1.96 indicates non-randomness. For a 99% confidence interval, if the absolute Z is greater than 2.58, it means that p<0.01, and the difference is even more significant—the null hypothesis can be rejected with greater confidence (for example, threshold ranking). .. an absolute Z-score of 1.96, 2, 2.58, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more, including all decimal points in between (e.g., 10.1, 10.6, 11.2, etc.), may provide a strong measure of statistical significance.
Regarding claims 4, 5, and 14:
“The method outputs the targeted state and the targeted state data from each iteration and wherein the method ends upon determining that a ratio of untargeted data to total data is greater than a threshold”
Nakamura et al. discloses that The OFLOOD unit stores the trajectory generated by OFLOOD which includes hidden dimensions (targeted state) in the storage unit (p. 0103). Figure 1, S111 also shows that trajectories will be stores (outputted) at the end of each iteration. Nakamura further discloses that if the density of protein structures in the cell is at or above an upper limit, such cell is determined to be a dense cell. If the density of protein structures in a cell is below an upper limit but equal to or above a lower limit, such cell is determined to be a medium cell. If the density of the protein structures in a cell is below a lower limit, the cell is determined to be a sparse cell. When a next lower layer after an upper layer is generated, only the middle cells out of the cells on the (p. 0077).
Regarding claim 6:
“Determining the current layer of data from the input molecular dynamics simulation data comprises sampling the input molecular dynamics simulation data to reduce a size of the current layer of data in a first iteration.”
Nakamura et al. discloses the FlexDice clustering algorithm of OFLOOD sampling unit performs clustering on a trajectory outputted as a result of a MD simulation that has a protein structure determined by experimentations as an initial structure (p. 0093). Clustering comprises sampling of protein structures (MD simulation data) according to their density into different cells (sampling) (p. 0077). It is inherent that sampling data reduces the size of the data.
Regarding claims 9 and 17:
“Separating the targeted state data from the current layer of data comprises a second clustering, the second clustering separating the targeted state data from the current layer of data using the targeted state.”
Nakamura et al. discloses that only medium cells among the cells of the higher-ranked layer are each divided into two in each axis direction (divided into four in total) (second clustering) (p. 0074).
Nakamura et al. discloses a second clustering, which is performed by FlexDice of OFLOOD unit, where clustering is performed after adding the hidden dimension (targeted state) to dimension group 11b step S2. When executing OFLOOD, the computing unit carries out clustering (second clustering) for structures that has the index dimensions/hidden dimensions (targeted state) included in the dimension group (p. 0039).
Regarding claim 20:
“The system of Claim 19, further comprising a display controlled by the processor to display the targeted state data.”
Nakamura et al. discloses a monitor (display) that is connected to a graphical processing device connected to the processor to display the instructions from the processor (p. 0050) Nakamura et al. also discloses that the OFLOOD results are visualized by displaying, on the monitor (p. 0062).
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have used a machine learning model, such as one disclosed by Harada, to perform the iterative method of anomaly detection of Nakamura (repeatedly executes the processing in steps S1 to 33 [0040], OFLOOD [0039]). Further it would have been obvious to have applied the known statistical technique of calculating and ranking an absolute value of probabilistic z-scores, as shown by Likić (p. 2961, col. 2, para. 1) and Xu (pg. 4, last para.), to the known method of Nakamura and Harada to detect abnormal data, because the absolute deviation performs well in detecting outliers in data offering a more efficient, automated, and more accurate anomaly detection. There would be a reasonable expectation of success in applying the technique of Likić and Xu to the method of Nakamura and Harada because they all use statistical methods to analyze anomaly in data.
Claims 7 and 15
Response to Applicant’s Arguments
Applicant states (pg. 19);
The dependent claims 7 and 15 separately recites subject matter not taught or suggested by any of the cited references, whether taken individually or in combination. At least for these reasons, claims 7 and 15 are believed to be patentable.
As stated above, the combination of Nakamura, Harada, Likid, and Xu teach all the limitations of claims 1, 11, and 19. Therefore, the above 103 rejection is maintained.
Claims 7 and 15 are rejected under 35 U.S.C 103 as being unpatentable over Nakamura, in view of Harada, Likić , and Xu, as applied to claims 1, 4-6, 9, 11, 14, 17, and 19-20 above, and further in view of Han (Research on ensemble model of anomaly detection based on autoencoder, 11 December 2020, Publisher: IEEE, Published in: 2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS); as cited in the 892 form dated 07/18/2024).
The limitations of claims 1 and 11, from which claims 7 and 15 depend, have been taught above.
Regarding claims 7 and 15:
“The abnormal data is separated from the current layer of data by an autoencoder.”
Nakamura et al. discloses out of the plurality of dimensions that express the structure of a substance; the computing unit sets (separates) a plurality of dimensions that are not included in the dimension group (current layer of data) as candidate dimensions (abnormal data). (p. 0037).
Nakamura, Harada, Likić , and Xu do not expressly teach separating the abnormal data from the current layer of data by an autoencoder.
However, Han et al. teaches separating abnormal data from current layer of data using an autoencoder (Section D The Proposed Ensemble Autoencoder (EAE) for anomaly detection (figure 3) teaches an autoencoder that extracts/separates abnormal/anomaly data).
Therefore, it would have been prima facie obvious to someone of ordinary skill in the art before the effective filling date of the claimed invention to separate abnormal data from current layer of data using an autoencoder. Doing so would make the abnormal data extraction from the original input data more robust as taught by Han et al. The person of ordinary skill would have had a reasonable expectation of success in selecting this combination because it would have done anomaly detection/separation of abnormal data.
Claims 10 and 18
Response to Applicant’s Arguments
Applicant's arguments filed 07/30/2026 have been considered but they are not yet persuasive.
Applicant states (pg. 20);
The dependent claims 10 and 18 separately recites subject matter not taught or suggested by any of the cited references, whether taken individually or in combination. At least for these reasons, claims 7 and 15 are believed to be patentable.
As stated above, the combination of Nakamura, Harada, Likid, and Xu teach all the limitations of claims 1, 11, and 19. Therefore, the above 103 rejection is maintained.
Claims 10 and 18 are rejected under 35 U.S.C 103 as being unpatentable over Nakamura, in view of Harada, Likić , and Xu, as applied to claims 1, 4-6, 9, 11, 14, 17, and 19-20 above, and further in view of Wu et al. (Identify High-Quality Protein Structural Models by Enhanced K-Means, March 22, 2017, Publisher: Biomed Research International; as cited in the 892 form dated 07/18/2024).
The limitations of claims 1, 9 and 19, from which claims 10 and 18 depend, have been taught above.
Regarding claims 10 and 18:
“The second clustering uses a measure of distance from a center of a cluster of the current layer of data and a threshold for the measure of distance.”
Nakamura et al. teaches a clustering algorithm, FlexDice. Nakamura, Harada, Likić , and Xu do not expressly teach that second clustering uses a measure of distance from a center of a cluster of the current layer of data and a threshold for the measure of distance. However, Wu et al. teach a K-mean clustering algorithm which is distance-based algorithm that uses a measure of distance from a center of cluster of data and a threshold for the measure of the distance (section 2.2. Classical K-Means Algorithm and 3D Distance Metrix, subsection 2.2.1 Classical K-Means Algorithm).
Therefore, it would have been prima facie obvious to someone of ordinary skill in the art before the effective filling date of the claimed invention to use a measure of distance from a center of a cluster of the current layer of data and a threshold for the measure of distance for the second clustering. Doing so would result in high quality models and efficient processing of large data sets as taught by Wu et al. (Conclusion Section). The person of ordinary skill would have had a reasonable expectation of success in selecting this combination because either of the clustering methods could be used for clustering of biological data.
Conclusion
No claims are allowed.
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.
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/G.S./Examiner, Art Unit 1686
/G. STEVEN VANNI/Primary patents examiner, Art Unit 1686
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