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 .
DETAILED ACTION
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
With regards to Claim 1, 8, and 15, the feature “the aging model being obtained by: determining a fitting relationship in the aging model, and determining parameters in the fitting relationship according to sample data of a corresponding type of the aging model” is indefinite as it is unclear how the sample data may be allocated into a category having already known aging model when the aging model is not available at the step of “dividing” and only would be obtained by using fitting relationship as claimed.
For the purpose of a compact prosecution, the Examiner interpreted “determining a fitting relationship in the aging model, and determining parameters in the fitting relationship according to sample data of a corresponding type of the aging model” as not being a pre-condition of “obtaining” the aging model but limitations that rather describe characteristics of the model while treating the feature “the aging model being obtained by ..” as “the aging model is characterized by…”
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Specifically, representative Claim 1 recites:
“A method for obtaining a capacity of a power battery, comprising:
collecting, by sensors, sample data of the power battery;
dividing, by a processor, the sample data into a plurality of categories, each of the categories having a corresponding aging model and a feature identifier, the feature identifier identifying features of sample data of a corresponding category, and the aging model being obtained by:
determining a fitting relationship in the aging model, and
determining parameters in the fitting relationship according to sample data of a corresponding type of the aging model;
acquiring, by the processor, battery state parameters of the power battery;
selecting, by the processor, an aging model from a plurality of aging models according to the battery state parameters; and
inputting, by the processor, the battery state parameters into the selected aging model to obtain the capacity of the power battery.”
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”.
Under the Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (process).
Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the groupings of subject matter that covers mathematical concepts - mathematical relationships, mathematical formulas or equations, mathematical calculations and mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion.
For example, steps of “determining a fitting relationship in the aging model, and determining parameters in the fitting relationship according to sample data of a corresponding type of the aging model” and “inputting, by the processor, the battery state parameters into the selected aging model to obtain the capacity of the power battery” are treated as belonging to the mathematical concepts grouping while the steps of “dividing, by a processor, the sample data into a plurality of categories, each of the categories having a corresponding aging model and a feature identifier, the feature identifier identifying features of sample data of a corresponding category” and “selecting, by the processor, an aging model from a plurality of aging models according to the battery state parameters” are treated as belonging to mental process grouping. These mental steps represent a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “dividing … the sample data into a plurality of categories, each of the categories having a corresponding aging model and a feature identifier, the feature identifier identifying features of sample data of a corresponding category” and “selecting … an aging model from a plurality of aging models according to the battery state parameters” in the context of this claim, encompasses a user manually dividing sample data into categories (based on “evaluation/ judgement” related to corresponding model/identifier and making a selection based on appropriate aging model out of available aging models based on state parameters (“threshold value information that stored therein a threshold value for each performance information of the respective components”). The former step, under the BRI, alternatively/additionally is treated as mathematical relationship step (MPEP 2106.04.II: “construing the claims in accordance with their broadest reasonable interpretation”).
Similar limitations comprise the abstract ideas of Claims 8 and 15.
Next, under the Step 2A, Prong Two, we consider whether the above claims that recites a judicial exception are integrated into a practical application.
The above claims comprise the following additional elements:
In Claim 1: A method for obtaining a capacity of a power battery, comprising: collecting, by sensors, sample data of the power battery; acquiring, by the processor, battery state parameters of the power battery;
In Claim 8: A device for obtaining a capacity of a power battery, comprising: at least one processor; and a memory coupled with the at least one processor, wherein the memory stores instructions, and when the instructions are executed by the at least one processor, the instructions cause the at least one processor to perform operations comprising: collecting, by sensors, sample data of the power battery; acquiring battery state parameters of the power battery;
In Claim 15: A non-transitory computer-readable storage medium, storing a computer program, wherein the computer program, when executed by a processor, causes the processor to perform operations comprising: collecting, by sensors, sample data of a power battery; acquiring battery state parameters of the power battery.
The additional elements in the preambles are recited in generality and represent insignificant extra-solution activity (field-of-use limitations) that is not meaningful to indicate a practical application.
The additional elements in the claims such as a processor (Claim 1), at least one processor; and a memory coupled with the at least one processor (Claim 8), and a non-transitory computer-readable storage medium, storing a computer program, wherein the computer program, when executed by a processor, causes the processor to perform operations (Claim 15) are examples of generic computer equipment (components) that are generally recited and not meaningful and, therefore, are not qualified as particular machines to indicate a practical application. The limitations that generically recite collecting, by sensors, sample data of a power battery; acquiring battery state parameters of the power battery (all independent claims) represent insignificant extra-solution activity of mere data gathering. According to the October update on 2019 SME Guidance such steps are “performed in order to gather data for the mental analysis step, and is a necessary precursor for all uses of the recited exception. It is thus extra-solution activity, and does not integrate the judicial exception into a practical application”.
Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
However, the above claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B analysis) because these additional elements/steps are well-understood and conventional in the relevant art based on the prior art of record.
The independent claims, therefore, are not patent eligible.
With regards to the dependent claims, claims 2-7, 9-14, and 16-20 provide additional features/steps which are part of an expanded abstract idea of the independent claims (additionally comprising abstract idea steps) and, therefore, these claims are not eligible without meaningful additional elements that reflect a practical application and/or additional elements that qualify for significantly more for substantially similar reasons as discussed with regards to Claim 1.
For example, additional elements in Claims 7 and 14 (types of battery parameters) are all recited in generality and not meaningful to indicate a practical application and/or qualify for significantly more.
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 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, 3-5, 7, 8, 10-12, 14-15, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Milutin Pajovic et al. (US 20200284846), hereinafter ‘Pajovic’ in view of Jing Sun et al. (US 20150066406), hereinafter ‘Sun’, and further in view of Shuai Wan et al. (CN 110163280), hereinafter ‘Wan’.
With regards to Claim 1, Pajovic discloses
A method for obtaining a capacity of a power battery (estimating capacity degradation of batteries [0001]), comprising:
collecting, by sensors, sample data of the power battery (Battery management system (BMS) monitors and manages operation of the associated battery based on measurements from a variety of sensors it employs [0002]; voltage and current measurements taken under the same conditions of a certain number of batteries of the same type [0007]; using empirical data collected from different batteries of the same type [0009]; The measurement log of Cell 7 contains measurements from charge-discharge cycles taken during 39 non-uniformly sampled test days over its lifetime [0079]);
dividing, by a processor, the sample data into a plurality of categories, each of the categories having a corresponding aging model identifying features of sample data of a corresponding category and the aging model (different capacity degradation models (i.e. “aging models”, emphasis added) determined for batteries of the same type can be clustered in a finite number of clusters. Each cluster can average and/or represent a degradation class of the batteries of a specific type. Hence, the problem of determining the right capacity degradation model for a specific battery is reduced to a problem of determining a degradation class of the battery and selecting (from the finite set of models) the capacity degradation model determined for the same degradation class [0011]; Some embodiments are based on empirical discovery that voltage of a battery, e.g., a tail voltage, depends not only on current capacity of the battery but also on degradation class of the battery [0013]; Additional information about degradation of a battery cell is obtained from its tail voltages (i.e. “feature of sample data”, emphasis added) [0054]; all tail voltages of battery cells clustered in the same degradation class 382 and measured during the discharge cycles when the corresponding battery cells has similar capacity value 380 are grouped together and processed with the goal to learn 386 a common tail voltage model 388 corresponding to the capacity value 380 and degradation class 382 [0062]; the tail voltage model 340 for each degradation class and admissible capacity value from that class can be obtained by simple averaging the measured tail voltages associated with the same class and capacity value … the clustering automatically yields degradation classes with the models for capacity traces and tail voltages [0073]; Fig. 2C, 230),
being obtained by: determining a fitting relationship in the aging model (capacity traces associated with a particular class are used to fit model parameters of some chosen empirical model to yield the capacity model for that class [0061]; model learning 386 can include fitting an empirical tail voltage model with measurements 384. In the case when a capacity trace represents one possible degradation class, each measured tail voltage of such a battery cell is one tail voltage model, parameterized with the corresponding capacity C. [0063]; the tail voltage model 340 for each degradation class and admissible capacity value from that class can be obtained by simple averaging the measured tail voltages associated with the same class and capacity value. In general, other approaches for modelling tail voltages are suitable for the proposed prediction methodology. As such, the tail voltage model for each degradation class and capacity value can be obtained by empirical curve fitting using the set of corresponding tail voltage measurements [0073]), and
determining fitting relationship according to sample data of a corresponding type of the aging model (model learning 386 can include fitting an empirical tail voltage model with measurements 384. In the case when a capacity trace represents one possible degradation class, each measured tail voltage of such a battery cell is one tail voltage model, parameterized with the corresponding capacity C. [0063]);
acquiring, by the processor, battery state parameters of the power battery (measure, in the operational stage, capacity and other measurements of a test battery [0008]; starting from the current, e.g., measured, capacity value of a battery [0009]);
selecting, by the processor, an aging model from a plurality of aging models according to the battery state parameters (selecting (from the finite set of models) the capacity degradation model determined for the same degradation class [0011]; select at least one battery cycle model closest to the battery cycle of the test battery [0015]; the segment of the selected capacity model starting from the measured capacity [0043]; Having this mapping, the degradation class can be selected for measured voltages. Knowing the degradation class, the capacity degradation model corresponding to that degradation class can be selected and used for determining capacity degradation of the battery over future period of time [0055]); and
inputting, by the processor, the battery state parameters into the selected aging model to obtain the capacity of the power battery (Having this mapping, the degradation class can be selected for measured voltages. Knowing the degradation class, the capacity degradation model corresponding to that degradation class can be selected and used for determining capacity degradation of the battery over future period of time [0055]; during the soft estimation, the embodiment selects a subset of the battery cycle models closest to the battery cycle of the test battery; retrieves a subset of capacity degradation models corresponding to the selected subset of battery cycle models; and estimates the future degradation of the capacity of the battery based on a combination of the retrieved subset of capacity degradation models. For example, in one implementation, the combination is a weighted combination of the retrieved subset of capacity degradation models with weights corresponding to distances between the battery cycle of the test battery and the selected battery cycle models [0071]).
However, Pajovic does not specifically disclose determining parameters in the fitting relationship according to sample data of a corresponding type of the aging model.
Sun discloses determining parameters in the fitting relationship according to sample data of a corresponding type of the aging model (determining the parameters of the model by fitting the voltage measures to the model [0010]; similar in [0043]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Pajovic in view of Sun to determine fitting relationship parameters as known in the art (conventional parameter estimation methods such as a least squares method can be directly used and the computational efficiency would be greatly improved … the parameter adaptation to fit individual cell data and aging status could be achieved through linear parameter identification, Sun [0044]).
Pajovic also does not specifically disclose dividing, by a processor, the sample data into a plurality of categories, each of the categories having a corresponding feature identifier, the feature identifier identifying features of sample data of a corresponding category and the aging model.
Wan discloses dividing, by a processor, the sample data into a plurality of categories, each of the categories having a corresponding feature identifier, the feature identifier identifying features of sample data of a corresponding category (It should be noted that, for processing classified management for enterprise mechanism of some region, it is necessary for each classification is provided in at least one core enterprise, namely the central point. other core enterprise in the same classification, must be less than or equal to a fixed value and the distance of the core enterprise, this fixed value called the maximum distance, can be represented as a radius area, p.4; a computer apparatus comprising at least one processor 1101, p.11).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Pajovic in view of Sun, and Wan to divide, by a processor, the sample data into a plurality of categories, each of the categories having a corresponding feature identifier, the feature identifier identifying features of sample data of a corresponding category as known in the art (Wan) while considering a corresponding aging model for the category.
With regards to Claim 3 and 4, Pajovic in view of Sun, and Wan discloses the claimed invention as discussed in Claim 1(3).
Pajovic also discloses dividing, by a processor, the sample data into a plurality of categories as discussed in Claim 1.
However, Pajovic does not specifically disclose selecting a clustering algorithm, and determining clustering parameters in the clustering algorithm; categorizing each point of the sample data as a core point or a boundary point of a cluster according to the clustering parameters; and configuring the categories according to the core point (Claim 3), wherein the clustering algorithm comprises a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm; and the clustering parameters comprise a radius of neighborhood and a neighborhood count threshold (Claim 4).
Wan discloses selecting a clustering algorithm (having a density of the noise-based clustering method (Density-Based Spatial Clustering of Applications Noise, DBSCAN) is a density-based spatial clustering algorithm. The algorithm will have sufficient density of area is divided into clusters, and noise found in the spatial database with clusters of any shape, which the cluster defining the maximum connected is density of set of points, p.5) and determining clustering parameters in the clustering algorithm (density of any point is point number contained in the circle region taking the point as the centre of a circle, E is the radius of. neighborhood region given the object radius is Ε is called the neighborhood of the object … the algorithm is based on the concept of clustering density, i.e. a certain area to cluster space contained in the number of object (point or other space object) is not less than a given threshold, p.5); categorizing each point of the sample data as a core point or a boundary point of a cluster according to the clustering parameters (As shown in FIG. 1d, the neighborhood may be represented by corresponding radius, set MinPts=3, then m,p, o, r is the core point (corresponding to the number of points in the neighborhood > MinPts=3), s, q is the boundary point (corresponding to the number of points in the neighborhood is 2<MinPts= 3, p.5); and configuring the categories according to the core point (the way of clustering can have a plurality of, according to the specific requirement, set mode clustering, the following after clustering method of each object belongs to only one category for example, describing an embodiment of the present invention, p.7), wherein the clustering algorithm comprises a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm (above); and the clustering parameters comprise a radius of neighborhood and a neighborhood count threshold (As shown in FIG. 5a, radius =2 of the neighborhood set, minimum sample number MinPts=2, p.7).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Pajovic in view of Sun, and further in view of Wan to select a clustering algorithm and determine clustering parameters in the clustering algorithm; categorize each point of the sample data as a core point or a boundary point of a cluster according to the clustering parameters; and configuring the categories according to the core point, wherein the clustering algorithm comprises a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and wherein the clustering parameters comprise a radius of neighborhood and a neighborhood count threshold as known in the art of clustering in data mining technology to accurately extract the valuable information (Wan, p.1).
With regards to Claim 5, Pajovic in view of Sun, and Wan discloses the claimed invention as discussed in Claim 1.
Pajovic also discloses selecting, by the processor, an aging model corresponding to the battery state parameters from a plurality of aging models as discussed in Claim 1.
However, Pajovic does not specifically disclose wherein the feature identifier comprises a clustering center; and the selecting, by the processor, an aging model corresponding to the battery state parameters from a plurality of aging models comprises: calculating a distance between the battery state parameters and a clustering center corresponding to each of the categories; and selecting an aging model having a shortest distance as the aging model corresponding to the battery state parameters.
Wan discloses the feature identifier comprises a clustering center as discussed in Claim 1.
Wan also discloses calculating a distance between parameters and a clustering center corresponding to each of the categories (It should be noted that, for processing classified management for enterprise mechanism of some region, it is necessary for each classification is provided in at least one core enterprise, namely the central point. other core enterprise in the same classification, must be less than or equal to a fixed value and the distance of the core enterprise, this fixed value called the maximum distance, can be represented as a radius area. Furthermore, the enterprise number in each classification, must be greater than or equal to a fixed value, this fixed value called minimum enterprise, wherein the distance can be more short in the coordinate system of the space distance and so on. distance formula between two points: the basic formula of coordinate function graph averages over two points for the distance between the calculated point, is one of distance formula. there are two points A, B and coordinates respectively are A (x1, y1), B (x2, y2), then the distance between two points of A and B is 1, K -, K-means clustering algorithm is as follows: randomly selecting K object as the clustering centre of the initial. then calculating each object and the distance between each seed clustering centre, distributing the each object to its nearest cluster centre distance. clustering centre and represents an object is assigned to the same clusterp.4); and selecting an aging model having a shortest distance as the aging model corresponding to the battery state parameters.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Pajovic in view of Sun, Wan to select, by the processor, an aging model corresponding to the battery state parameters from a plurality of aging models by using a feature identifier (a clustering center) corresponding to the battery state parameters and calculating a distance between a clustering center corresponding to each of the categories; and select an aging model having a shortest distance as the aging model corresponding to the battery state parameters similar to selecting a category because of advantages of this procedure as known in the art of clustering (Each cluster area, under the condition of meeting the condition comprises a minimum enterprise of, any one of the object cluster and the distance of the core point, is less than or equal to a given fixed distance so that the maximum distance of the cluster core enterprise and non-enterprise does not exceed the predetermined threshold, and each classification cluster in the enterprise not less than minimum. application requirement satisfies the service, solving the problem that the current technology that the clustering result obtained when dividing the category through the existing clustering method cannot satisfy the technical problem application demand, and the clustering process does not depend on any parameters, Wan, p.6).
With regards to Claim 7, Pajovic additionally discloses the battery state parameters comprise: at least two of a current, a voltage, a temperature, state of charge, storage time, a depth of discharge, and coulombic efficiency (A battery diagnostic system stores capacity degradation models for batteries of a specific type mapped to sets of battery cycle models formed by voltages and/or currents measured at different capacities, Abstract).
With regards to Claims 8 and 15, Pajovic in view of Sun and Wan discloses the claim limitations as discussed in Claim 1.
In addition, with regards to Claims 8 and 15, Pajovic discloses a device for obtaining a capacity of a power battery, comprising: at least one processor; and a memory coupled with the at least one processor, wherein the memory stores instructions and a non-transitory computer-readable storage medium, storing a computer program, wherein the computer program, when executed by a processor, causes the processor to perform operations (Yet another embodiment discloses a non-transitory computer readable storage medium embodied thereon a program executable by a processor for performing a method [0017]; Fig. 1A).
With regards to Claims 10 and 17, Pajovic in view of Sun and Wan discloses the claim limitations as discussed in Claims 3 and Claims 8 and 15, respectively.
With regards to Claims 11 and 18, Pajovic in view of Sun and Wan discloses the claim limitations as discussed in Claims 4 and Claims 8 and 15, respectively.
With regards to Claims 12 and 19, Pajovic in view of Sun and Wan discloses the claim limitations as discussed in Claims 5 and Claims 8 and 15, respectively.
Claims 2, 9, and 16 are rejected under 35 U.S.C. 103 as being unpatentable Pajovic in view of Sun, Wan, in further view of Shi-chun Yang et al. (CN 111999665), hereinafter ‘Yang’.
With regards to Claim 2, Pajovic in view of Sun, and Wan discloses the claimed invention.
Pajovic also discloses a plurality of sets of data taken under the same condition [0007, 0015] for the same model of the power battery (the capacity degradation model determined for the same degradation class [0011]).
However, Pajovic does not specifically disclose the sample data of the power battery comprises: a plurality of sets of data for a same model of the power battery under a plurality of vehicle driving conditions.
Yang discloses sampling data under a plurality of vehicle driving conditions (a lithium ion battery aging test method based on micro-mechanism under automobile driving condition, p.1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Pajovic in view of Sun, Wan, and further in view of Yang to sample data of the power battery for a same model of the power battery under a plurality of vehicle driving conditions because actual working condition of the power battery in the whole vehicle driving is changed in real time that would affect an aging model under consideration to accurately estimate aging (finishing the combined simulation of the vehicle model and the control strategy model, converting the vehicle standard driving condition into equivalent test condition of the lithium ion battery, Yang, p.2).
With regards to Claims 9 and 16, Pajovic in view of Sun and Wan discloses the claim limitations as discussed in Claims 2, and Claims 8 and 15, respectively.
Claims 6, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable Pajovic in view of Sun, Wan, in further view of Humberto E. Garcia et al. (US 20180143257), hereinafter ‘Garcia’.
With regards to Claim 6, Pajovic in view of Sun, and Wan discloses the claimed invention.
Pajovic also discloses fitting relationship as discussed in Claim 1.
However, Pajovic does not specifically disclose wherein the fitting relationship comprises polynomial fitting, neural network fitting, or regression tree fitting.
Garcia discloses wherein the fitting relationship comprises polynomial fitting, neural network fitting, or regression tree fitting (Mapping algorithms used in the present disclosure to convert the state estimation data into the performance metrics include polynomial fitting, neural network models, and Auto Regressive Moving Average (ARMA) [0048]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Pajovic in view of Sun, Wan, and further in view of Garcia that the fitting relationship comprises polynomial fitting, neural network fitting, or regression tree fitting known in the art to improve aging models (learning module 210 thus constructs models (e.g., aging models) that best fit estimations with the training data, Garcia [0073]).
With regards to Claims 13 and 20, Pajovic in view of Sun and Wan discloses the claim limitations as discussed in Claims 6, and Claims 8 and 15, respectively.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXANDER SATANOVSKY whose telephone number is (571)270-5819. The examiner can normally be reached on M-F: 9 am-5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Catherine Rastovski can be reached on (571) 270-0349. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALEXANDER SATANOVSKY/
Primary Examiner, Art Unit 2863