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 .
Status of Claims
This Office action is in reply to filing by applicant on 09/11/2025.
Claim 1 was amended by Applicant.
Claims 2 – 9 remain as original.
Claims 1 – 9 are currently pending and have been examined.
The prior 35 USC 103 claim rejections set forth in the Non-Final rejection of 06/12/2025 as to claims 1 – 9 are maintained in view of Applicant's arguments and amendments.
THIS ACTION IS MADE FINAL.
Response to Arguments
There are no new grounds of rejection herein as to any of the claims.
Examiner notes that due to Applicant’s substantial amendments incorporated into the claims of 09/11/2025, examiner replaced the prior secondary reference of Chenglin with the new secondary reference of Khoury. The other two citations used in the prior 35 USC 103 rejection analysis, namely, Garcia and Negotia, remain, and they are otherwise now combined with the new reference Khoury in the below 35 USC 103 analysis.
Given the above, where the now the secondary reference, Khoury and the previously used two references of Garcia and Negotia are now all combined in the claim analysis, Applicant’s 35 USC 103 arguments are moot. Please see the detailed 35 USC 103 analysis below.
Generally as to obviousness, examiner submits that it is determined on the basis of the evidence as a whole and the relative persuasiveness of the arguments. See In re Oetiker, 977 F.2d 1443, 1445, 24 USPQ2d 1443, 1444 (Fed. Cir. 1992); In re Hedges, 783 F.2d 1038, 1039, 228 USPQ 685,686 (Fed. Cir. 1992); In re Piasecki, 745 F.2d 1468, 1472, 223 USPQ 785,788 (Fed. Cir. 1984); and In re Rinehart, 531 F.2d 1048, 1052, 189 USPQ 143,147 (CCPA 1976). Using this standard, examiner submits that the burden of presenting a prima facie case of obviousness was successfully established in the prior Office Action of 06/12/2025, and also respecting the pending amended claim set of 09/11/2025, as seen below.
Examiner recognizes that references cannot be arbitrarily altered or modified, and that there must be some reason why a person having ordinary skill in the relevant art would be motivated to make the proposed modifications. Although the motivation or suggestion to make modifications must be articulated, it is respectfully submitted that there is no requirement that the motivation to make modifications must be expressly articulated within the references themselves. References are evaluated by what they suggest to one versed in the art, rather than by their specific disclosures, In re Bozek, 163 USPQ 545 (CCPA 1969).
Examiner also notes that the motivation to combine the applied references is, where appropriate in the below detailed analysis pursuant to 35 USC 103, additionally accompanied by select passages from the respective references which specifically support that particular motivation. It is also respectfully submitted that motivation based on the logic and scientific reasoning of one ordinarily skilled in the art at the time of the invention, which evidence can also support a finding of obviousness, is otherwise provided in the detailed 35 USC 103 analysis of the claim set below. In re Nilssen, 851 F.2d 1401, 1403, 7 USPQ2d 1500, 1502 (Fed. Cir. 1988) (references do not have to explicitly suggest combining teachings); Ex parte Clapp, 227 USPQ 972 (Bd. Pat. App. & Inter. 1985) (examiner must present convincing line of reasoning supporting rejection); and Ex parte Levengood, 28 USPQ2d 1300 (Bd. Pat. App. & Inter. 1993) (reliance on logic and sound scientific reasoning).
Examiner recognizes that obviousness can only be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to a person of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988) and In re Jones, 958 F.2d 347.
Claim Rejections – 35 USC 103
In the event the determination of the status of the application as subject to AIA 35 USC 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 USC 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 USC 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.
Claims 1 – 9 are rejected pursuant to 35 USC 103 as being unpatentable over Garcia (US20180143257A1) in view of Khoury (US20180130095A1) and in further view of Negoita (US20240217388A1).
Regarding claim 1:
Garcia discloses:
A method for designing an accelerated battery aging testing protocol from battery electric vehicle usage data, the method comprising: (“This model characterizes how the battery is anticipated to age with time as it is operated. As different failure mechanisms will age the battery differently at different aging rates, the prediction performance highly depends on several factors and strategies such as correctly identifying the dominant failure mechanism for the operational condition considered and thus using the corresponding aging model, using more than a single aging model in order to consider different failure mechanisms, or even using aggregated time-varying aging models to consider situations where more than one failure mechanism may be aging the battery”, [0114]) and (“Typical applications include … aircrafts, and hybrid plug-in hybrid electric vehicles (PHEV), and electric vehicles.”, [036]);
creating an initial search space database based on a collection of vehicle usage data, the usage data … including … current demand over a first time frame; (“Embodiments of the present disclosure may be used within an online Battery Condition Monitoring (BCM) system that tracks changes in battery performance in order to estimate and predict battery condition metrics (e.g., SOC/SOH/RUL/EOL) and to optimally manage power based on user requirements, system usage models, and environmental conditions. Inputs to the BCM system may include user requirements (e.g., user behavior, survivability needs), environmental conditions (e.g., current and anticipated power demand, limitations, uncertainties), performance data, and sensor data (e.g., passive and active measurements).”, [052]) and (“Typical applications include … hybrid plug-in hybrid electric vehicles (PHEV), and electric vehicles.”, [036]) and (“The observed, what-if, and forecast module 110 may include databases of historically observed operation as well as forecast predictions and what-if assumptions to construct, refine, or a combination thereof, various models (e.g., aging models) and to support estimations of battery future conditions”, [069]) and (“For monitoring, the aging model corresponding to the considered condition at hand is used to implement algorithms (e.g., particle filtering) that estimate battery SOH and RUL as battery data and features are respectively collected and extracted periodically during offline testing.”, [040]);
generating a synthetic profile including a sequence of elements having a battery current and a battery state of charge (SOC) for selected segments of the specific segments, (“Thus, this module first selects a given operational profile, which then translates to a specific aging model used to predict future values of battery parameters, battery states, or a combination thereof.”, [0121]) and (“The training of these mapping models and algorithms is achieved by using measurements and characterizations specifically collected for training at different operating conditions, including diverse temperature and charge/discharge profiles. This training data can be collected for different options of battery chemistries, capacities, configurations, and other possibilities.”, [051]) and (“As non-limiting examples, input data 102 include impedance spectra, temperatures, offload/underload conditions, voltages, and currents collected at sampling rates that are accordingly selected for the applications at hand.”, [061]) and (“Based on this description, it is implicitly indicated that one embodiment of the present disclosure employs a feature extraction module 220 that uses impedance spectra data, although other input data (e.g., voltage and current) may be used. On the other hand, temperature data are used to accordingly modify baseline aging mappings and databases learned and stored when necessary.”, [086]) and (“FIG. 3 is a block diagram showing process details of an online battery health estimation and health prediction system. FIG. 3 augments the description of the present disclosure as schematized in FIGS. 2 and 1 in that it further separates the tasks of estimation and prediction as well as introduces four distinct computational paths for computing each of the four identified elements of output data 195.”, [079]); battery currents, specific segments (paths traveled), and a battery’s state of charge (SOC) may be monitored in a profile;
defining an optimization for accelerated aging of the battery; and executing a genetic algorithm (GA) that generates the accelerated battery aging testing protocol requiring a second timeframe, shorter than the first timeframe, based on the optimization. (“This information is then used by the present disclosure to construct an aging model or a multiplicity of aging models considering different failure mechanisms of the battery through learning algorithms (e.g., relevance vector machines (RVM), fuzzy logic, and neural networks). These aging models will age the battery in distinct manners and at different rates. For monitoring, the aging model corresponding to the considered condition at hand is used to implement algorithms (e.g., particle filtering) that estimate battery SOH and RUL as battery data and features are respectively collected and extracted periodically during offline testing.”, [040]) and (“The feature extraction module is also configured for performing a decision fusion algorithm for combining the geometric parameters and the optimized parameters to develop new internal battery parameters including at least a constant phase element exponent, electrolyte resistance, and charge transfer resistance. The method also includes a state estimation module for updating an internal state model of the battery responsive to the new internal battery parameters, a health estimation module for processing the internal state model to determine a present battery health including one or both of a state-of-health (SOH) estimation and a state-of-charge (SOC) estimation for the battery, and a communication module for communicating one or more of the SOH estimation and the SOC estimation to a user, a related computing system, or a combination thereof.”, [005]) and (“More particularly, embodiments of the present disclosure include a battery diagnostic and prognostic architecture using methods, algorithms, and models, wherein the internal conditions of the battery can be estimated and predicted”, [038]) and (“As different failure mechanisms will age the battery differently at different aging rates, the prediction performance highly depends on several factors and strategies such as correctly identifying the dominant failure mechanism for the operational condition considered and thus using the corresponding aging model, using more than a single aging model in order to consider different failure mechanisms, or even using aggregated time-varying aging models to consider situations where more than one failure mechanism may be aging the battery, with their relative dominance potentially changing with time as time progresses.”, [0114]), relative time varying models (time-varying aging models) as above have at least one testing time frame which is less than another.
Garcia does not expressly disclose, but Khoury teaches:
[the usage data further including] …. representing all driving conditions including rural and urban driving conditions, the usage data including (i) first usage data corresponding to rural setting driving conditions and (ii) second usage data corresponding to urban setting driving conditions, the usage data defined as … (“For example fleet A from Manufacturer A and fleet B from manufacturer B can both have similar loss ratios if traditional methods were used, but the root cause of this similarity could be very different, something that traditional methods cannot distinguish: fleet B could have low claims because fleet B is driven mainly in rural areas, whereas fleet A can have low claims because it has excellent software even when driven mostly in urban areas. This distinction cannot be made without capturing the route characteristics and including them as factors in the calculation of profitability. The risk adjusted system will be such that risk pool will be determined as a factor of (1) how much the car is driven (risk adjusted mileage adjusted for where it is driven), (2) how the car was driven, (3) the cost of potential claim, and (4) who/what is driving the car.”, [0135]) and, as to storing a database as above, … (“The collection of the driving information is enabled and disabled based on location and movement of the device. The method further includes (i) encoding the driving information and transmitting the encoded driving information to a server, (ii) storing, in a database associated with the server, an identifier associated with the driver and the encoded driving information, and (iii) determining, and storing in the database, predicted future typical route segments that the driver is likely to travel over a certain period of time and associated times of day based on the encoded driving information, and (iv) classifying the driver into one or more groups based on the encoded driving information.”, [007]) and (“The path of each route and the area types travelled are stored in the route database 939 and compared to the demographics database 938 as well as all the parameters, attributes, and factors of the route 940.”, [070]); vehicle usage data are stored for all routes, including rural and urban routes;
the specific segments corresponding to use events including a first plurality of segments associated the first usage data and a second plurality of segments associated with the second usage data; (“The method further includes (i) encoding the driving information and transmitting the encoded driving information to a server, (ii) storing, in a database associated with the server, an identifier associated with the driver and the encoded driving information, and (iii) determining, and storing in the database, predicted future typical route segments that the driver is likely to travel over a certain period of time and associated times of day based on the encoded driving information,”, [007]) and (“In some embodiments, the driving information can be matched with route segments known to be associated with self-driving vehicles. The device and driver identifier can be associated with a self-driving capable vehicle status, and time periods, mileage, and route segments during which the vehicle is driven in self-driven mode or driven manually by the driver may be identified. The driving information can be allocated according to the identified time periods, mileage, and route segments to affect the classification of the driver or self-driving system.”, [012]) and (“To further minimize data and battery usage, the software on the mobile device can operate in the background as a service to measure various on-board sensors and cell-ID changes in order to determine whether the device owner is in a vehicle and whether to initiate GPS measurements, which are more resource intensive.”, [029]), battery usage data over a plurality of “first” and “second “ segments may be acquired;
identifying and grouping segments of the specific segments that share current similarities;(“The method further includes (i) encoding the driving information and transmitting the encoded driving information to a server, (ii) storing, in a database associated with the server, an identifier associated with the driver and the encoded driving information, and (iii) determining, and storing in the database, predicted future typical route segments that the driver is likely to travel over a certain period of time and associated times of day based on the encoded driving information,”, [007]) and (“In some embodiments, the driving information can be matched with route segments known to be associated with self-driving vehicles. The device and driver identifier can be associated with a self-driving capable vehicle status, and time periods, mileage, and route segments during which the vehicle is driven in self-driven mode or driven manually by the driver may be identified. The driving information can be allocated according to the identified time periods, mileage, and route segments to affect the classification of the driver or self-driving system.”, [012]); different route segments and different time periods may be identified and grouped according to their similarities;
the selected segments selected proportionally to an amount of time the vehicle is used in all driving settings; (“In some embodiments, the driving information can be matched with route segments known to be associated with self-driving vehicles. The device and driver identifier can be associated with a self-driving capable vehicle status, and time periods, mileage, and route segments during which the vehicle is driven in self-driven mode or driven manually by the driver may be identified. The driving information can be allocated according to the identified time periods, mileage, and route segments to affect the classification of the driver or self-driving system.”, [012]), the driving segments can be selected proportional to the time driven over them;
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application to have modified Garcia to incorporate the teachings of Khoury because Garcia would be more efficient, marketable, and versatile if it could represent different driving conditions / characteristics including all, urban, and rural driving conditions that drivers regularly drive, to better judge and estimate battery usage and capacity as done in Khoury . (This distinction cannot be made without capturing the route characteristics and including them as factors in the calculation of profitability. The risk adjusted system will be such that risk pool will be determined as a factor of (1) how much the car is driven (risk adjusted mileage adjusted for where it is driven), (2) how the car was driven, (3) the cost of potential claim, and (4) who/what is driving the car.”, see Khoury at [0135], and also see Khoury as above at [007, 070]).
The combination of Garcia and Khoury does not expressly disclose, but Negoita teaches:
performing data compression including classifying the database into specific segments representing use events, (“Various apparatus, systems and methods are disclosed herein relating to controlling operation of a vehicle. In some illustrative embodiments, a battery management system is disclosed for processing a state of health for a battery, comprising: at least one data storage … extract one or more data features from a multivariate time series data associated with a plurality of vehicles, the multivariate time series data comprising battery information data for the vehicles;”, [004]) and (“Turning back to FIG. 3A, once linear interpolation is performed in block 308, the interpolated time series data may then be down sampled in block 310 to compress the data to one or more configured time periods 310. In this example, the down sampling may be performed on any of the shown 6-, 8-, 10-, 12- and 14-minute time scales. Of course, other time periods may be used, depending on the application.”, [047]);
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application to have modified Garcia to incorporate the teachings of Negoita because Garcia would be more efficient and versatile if it could compress data to make it better fit any particular testing time period, as done in Negoita. (“The feature engineering of block 312 performs preprocessing steps that transform the raw data into features that can be used in machine learning algorithms, such as predictive models (314, 318, 320, 336).”, [047]).
Regarding claim 2
The combination Garcia, Khoury , and Negotia have the limitations of claim 1:
Garcia further teaches:
wherein the segments are selected proportionally to an amount of time the battery electric vehicle is used in all use events. Examiner broadly interprets this claim to include that testing periods represent use patterns, … (“For monitoring, the aging model corresponding to the considered condition at hand is used to implement algorithms (e.g., particle filtering) that estimate battery SOH and RUL as battery data and features are respectively collected and extracted periodically during offline testing.”, [040]).
Regarding claim 3:
The combination Garcia, Khoury , and Negotia have the limitations of claim 1:
Garcia further teaches:
wherein the synthetic profile is statistically representative of the initial search space dataset. Examiner broadly interprets this claim to include that the profile represents the dataset, … testing periods represent use patterns, … (“The training of these mapping models and algorithms is achieved by using measurements and characterizations specifically collected for training at different operating conditions, including diverse temperature and charge/discharge profiles. This training data can be collected for different options of battery chemistries, capacities, configurations, and other possibilities. As the above description mainly pertains to the task of “estimating” battery condition metrics, a similar information flow is applied when “predicting” battery condition metrics such as RUL and EOL. However, an additional set of models are used with prediction to forecast what may be the battery conditions under assumed future operating conditions at diverse time horizons.”, [051]).
Regarding claim 4:
The combination Garcia, Khoury , and Negotia have the limitations of claim 1:
Garcia further teaches:
wherein generating the synthetic profile for selected segments includes selecting segments representing high stress on the battery. (“As non-limiting examples, input data 102 include impedance spectra, temperatures, offload/underload conditions, voltages, and currents collected at sampling rates that are accordingly selected for the applications at hand.”, [061]) and (“These data-driven models, corresponding to input/output characterizations of the particular battery systems of interest, are computed or tuned based on observations collected during experimental testing, in-field operations, or combinations thereof. To accomplish the tuning, the training and learning module 210 may use inputs from existing internal state data 212 as well as current and historical external data 214, such as, for example, temperature and load conditions. The training and learning module 210 thus constructs models (e.g., aging models) that best fit estimations with the training data. With these mappings tuned and embedded within it, the BCM system is ready for operation.”, [073]).
Regarding claim 5:
The combination Garcia, Khoury , and Negotia have the limitations of claim 4:
Garcia further teaches:
wherein generating the synthetic profile for selected segments includes removing segments representing low stress on the battery. (“As non-limiting examples, input data 102 include impedance spectra, temperatures, offload/underload conditions, voltages, and currents collected at sampling rates that are accordingly selected for the applications at hand.”, [061]) and (“These data-driven models, corresponding to input/output characterizations of the particular battery systems of interest, are computed or tuned based on observations collected during experimental testing, in-field operations, or combinations thereof. To accomplish the tuning, the training and learning module 210 may use inputs from existing internal state data 212 as well as current and historical external data 214, such as, for example, temperature and load conditions. The training and learning module 210 thus constructs models (e.g., aging models) that best fit estimations with the training data. With these mappings tuned and embedded within it, the BCM system is ready for operation.”, [073]).
Regarding claim 6:
The combination Garcia, Khoury , and Negotia have the limitations of claim 1:
Garcia further teaches:
wherein generating the synthetic profile for selected segments includes selecting segments representative of driving conditions. (“These data-driven models, corresponding to input/output characterizations of the particular battery systems of interest, are computed or tuned based on observations collected during experimental testing, in-field operations, or combinations thereof. To accomplish the tuning, the training and learning module 210 may use inputs from existing internal state data 212 as well as current and historical external data 214, such as, for example, temperature and load conditions. The training and learning module 210 thus constructs models (e.g., aging models) that best fit estimations with the training data. With these mappings tuned and embedded within it, the BCM system is ready for operation.”, [073]).
Regarding claim 7:
The combination Garcia, Khoury , and Negotia have the limitations of claim 6:
Garcia further teaches:
wherein generating the synthetic profile for selected segments includes selecting segments representative of rural driving conditions. Examiner broadly interprets this claim to include that the a simulated driving conditions are representative of driving conditions, … (“These data-driven models, corresponding to input/output characterizations of the particular battery systems of interest, are computed or tuned based on observations collected during experimental testing, in-field operations, or combinations thereof. To accomplish the tuning, the training and learning module 210 may use inputs from existing internal state data 212 as well as current and historical external data 214, such as, for example, temperature and load conditions. The training and learning module 210 thus constructs models (e.g., aging models) that best fit estimations with the training data. With these mappings tuned and embedded within it, the BCM system is ready for operation.”, [073]).
Regarding claim 8:
The combination Garcia, Khoury , and Negotia have the limitations of claim 1:
Garcia further teaches:
wherein generating the synthetic profile for selected segments includes selecting one of driving conditions and battery charging conditions. Examiner broadly interprets this claim to include that driving / battery charging are relevant to the battery profile, … (“These data-driven models, corresponding to input/output characterizations of the particular battery systems of interest, are computed or tuned based on observations collected during experimental testing, in-field operations, or combinations thereof. To accomplish the tuning, the training and learning module 210 may use inputs from existing internal state data 212 as well as current and historical external data 214, such as, for example, temperature and load conditions. The training and learning module 210 thus constructs models (e.g., aging models) that best fit estimations with the training data. With these mappings tuned and embedded within it, the BCM system is ready for operation.”, [073]).
Regarding claim 9:
The combination Garcia, Khoury , and Negotia have the limitations of claim 1:
Garcia further teaches:
wherein defining an optimization includes leveraging prediction of degradation of the battery resulting from applying a synthetic current sequence to a battery model. Examiner broadly interprets this claim to include that the synthetic cycle represents actual driving conditions, … (“Several processes occur during charge and discharge reactions including chemical, electrochemical, and diffusion processes. The reactions and reactants that are present at each active mass surface as well as the morphological structure and availability of active materials determine the battery's electrical behavior and performance under different operating conditions. The active material structure and its associated conductivity, which are affected by the given operating conditions, can thus have an impact on battery parameters like capacity and internal resistance. For example, higher temperatures may lead to increased ion energy and mobility, allowing a greater surface area to participate in reactions, thus lowering the battery's internal resistance, but also reducing overall life expectancy.”, [003]).
CONCLUSION
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee 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.
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see attached form 892.
Jones (US6526361B1) - Method and apparatus for battery evaluation and classification applies transient microcharge and/or microload pulses to an automotive battery. Classification is made on the basis of analysis of the resultant voltage profile or portions or dimensions thereof. In one embodiment the analysis utilizes a neural network or algorithm to assess a microcycle sequence of microload/microcharge tests utilizing one of a series of battery parameters including impedance as well as voltage characteristics to effect classification. Another embodiment adopts an optimized (not maximum) level of prior test-based data-training for a self-organizing neural network. A third embodiment utilizes prior test data correlation to enable algorithm-based classification without use of a neural network.
Xu (US20220252670A1) - Used batteries are screened based on a measured Sectional Constant-Current Impulse Ratio (SCCIR). A used battery is partially charged over a small voltage range using a Constant Current (CC) until a voltage target is reached, and the current integrated to obtain the CC charge applied, Qcc. Then the battery continues to be charged using a Constant Voltage (CV) of the voltage target until the charging current falls to a midrange current target before the battery is fully charged. The current is integrated over the CV period to obtain the CV charge applied, Qcv. The measured SCCIR is the ratio of Qcc to (Qcc+Qcv) and is input to a calibration curve function to obtain a modeled State of Health (SOH) value for sorting. The calibration curve function is obtained by aging new batteries to obtain SCCIR and SOH data that are modeled using a neural network.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW COBB whose telephone number is (571) 272-3850. The examiner can normally be reached 9 - 5, M - F.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter Nolan, can be reached at (571) 270-7016. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/MATTHEW COBB/Examiner, Art Unit 3661
/PETER D NOLAN/Supervisory Patent Examiner, Art Unit 3661