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
This Office Action is in response to communications filed on 10/03/2025. No claims were amended or canceled. Likewise, claims 1-20 remain pending for examination.
Title 35, U.S. Code
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior office action.
Claim Rejections - 35 USC § 102
Claims 1, 8 and 15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ameyoe et al. (FR 3032919) et al.
Regarding claim 1, (Original) Ameyoe teaches a method (Figs 1-4) comprising:
receiving metrics associated with a vehicle (Pg. 7 lines 18-26);
generating a deviation vector based on the metrics and a plurality of aggregated values corresponding to the metrics (Pg. 17 lines 6-9, Pg. 19, lines 20-27);
computing a driver update value based on the deviation vector and a plurality of model parameters, each of the plurality of model parameters corresponding to the metrics (Pg. 3, lines 19-29); and
computing a driver score based on the driver update value, a previous score, and a learning rate (Pg 20, lines 6-14).
Regarding claim 8, (Original) Ameyoe teaches a non-transitory computer-readable storage medium for tangibly storing computer program instructions capable of being executed by a computer processor, the computer program instructions defining steps of:
receiving metrics associated with a vehicle (Pg. 7 lines 18-26);
generating a deviation vector based on the metrics and a plurality of aggregated values corresponding to the metrics (Pg. 17 lines 6-9, Pg. 19, lines 20-27);
computing a driver update value based on the deviation vector and a plurality of model parameters, each of the plurality of model parameters corresponding to the metrics (Pg. 3, lines 19-29); and
computing a driver score based on the driver update value, a previous score, and a learning rate (Pg 20, lines 6-14).
Regarding claim 15, (Original) Ameyoe teaches a device comprising:
a processor (Figs 1-4) configured to:
receive metrics associated with a vehicle (Pg. 7 lines 18-26);
generate a deviation vector based on the metrics and a plurality of aggregated values corresponding to the metrics (Pg. 17 lines 6-9, Pg. 19, lines 20-27);
compute a driver update value based on the deviation vector and a plurality of model parameters, each of the plurality of model parameters corresponding to the metrics (Pg. 3, lines 19-29); and
compute a driver score based on the driver update value, a previous score, and a learning rate (Pg 20, lines 6-14).
Claim Rejections - 35 USC § 103
Claims 1-3, 6-10, 13-17 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Carver et al. (U.S. Patent Application Pub. 2015/0294422) in view of Ameyoe et al. (FR 3032919).
Regarding claim 1, Carter teaches a method comprising:
receiving metrics associated with a vehicle (¶ 023; "An embodiment advances the art of risk management by utilizing the combined Intelligence of a larger off-board data set, deep-earning methods of event correlation and myriad nationwide real-time data (including but not limited to wireless transmission) sources to describe a predictive algorithm for dynamic risk
rating based on variations in both the vehicle performance and driver score against the predicted norm for the environmental conditions,”); and
computing a driver score based on the driver update value, a previous score, and a learning rate (¶ 023;"utilizing the combined intelligence of a larger off-board data set, deep-leaning methods of event correlation and myriad nationwide real-time data (including but not limited to wireless transmission) sources to describe a predictive algorithm for dynamic risk rating based on variations in both the vehicle performance and driver score against the predicted norm for the environmental conditions,”).
Carter does not explicitly mention generating a deviation vector based on the metrics and a plurality of aggregated values corresponding to the metrics;
computing a driver update value based on the deviation vector and a plurality of model parameters, each of the plurality of model parameters corresponding to the metrics. Ameyoe from an analogous art teaches a method for detecting a change in conductor behavior uses a cybernetic model defined by a first measurement vector, an output vector for estimating one or more driver-generated data, a parameterization vector, and a temporal state vector of the model (Abstract) and the concepts of generating a deviation vector based on the metrics and a plurality of aggregated values corresponding to the metrics (Pg. 17 lines 6-9, Pg. 19, lines 20-27);
computing a driver update value based on the deviation vector and a plurality of model parameters, each of the plurality of model parameters corresponding to the metrics (Pg. 3, lines 19-29). Therefore, it would have been obvious for one of ordinary skill in the art at the time of filing the invention to combine the driver monitoring and scoring system of Carter with generating a deviation vector based on the metrics and a plurality of aggregated values corresponding to the metrics; and computing a driver update value based on the deviation vector and a plurality of model parameters, each of the plurality of model parameters corresponding to the metrics, as taught by Ameyoe in order to calculate a covariance matrix on output values).
Regarding claim 2, Carter and Ameyoe teach the method of claim 1, and Carter teaches further comprising generating the aggregated values by:
receiving, for a plurality of road segments, corresponding metrics from a plurality of drivers (¶ 004; “a determination of risk based on granular driving history data accumulated for specific road segments”); and
aggregating, for each of the plurality of road segments, the corresponding metrics (¶ 050; "For the application of FAIR scores to regulated fleets with commercial drivers monitored for Driver Fitness (e.g. applications for EKG and glucose levels) higher quality data is required for the Driver scoring database 107 and the driver safety index 108. Unique to an embodiment of the FAIR score method Is the aggregation and mining of all data sets from various networks prior to the application of the location specific data 106 and the environmental data 104, within the database,”; (¶ 093; “The task of creating a predictive model from the Indices can be challenging because they are multivariate and not a coherent representation of actual exposure. The method described works best when applied to large samples of data for which peer-to-peer analytics can be applied and exceedences by road segmented ranked and stored for analytics during low density traffic periods. Regardless of traffic density for the trip segment, the same reverse chronological method of pattern methods described in the Individual trip scores are applied to determine the risk density functions used to produce the indices found In the results database 101 at the fleet level. The resulting indices are therefore available to use directly within the fleet's risk management program,”).
Regarding claim 3 Carter and Ameyoe teach the method of claim 2, and Carter teaches wherein aggregating the corresponding metrics further comprises averaging the corresponding metrics (¶ 093; "These indices are then summed for a common group of trips, or period of time using a time weighted average (proportional to exposure) to produce the fleet level score stored, along with the Individual scores, by VIN number in the results database 101,").
Regarding claim 6 Carter and Ameyoe teach the method of claim 1, and Carter teaches further comprising calculating the model parameters via a statistical learning methodology (¶ 023; "Dynamically mining variables to compute these indices results from a machine learning algorithm to detect changes in driver behavior,”).
Regarding claim 7 Carter and Ameyoe teach the method of claim 6, and Carter teaches wherein the statistical learning methodology is trained using a combination of video, telematics, and externally-obtained data ( ¶ 026; "An optionally advantageous aspect of the usage based method of underwriting lies in the existence of a process capable of gathering exposure information from a wide network of sensors, including video, both inside and outside the vehicle,"; ¶ 043 “the system 100 includes onboard devices configured to provide telemetric data such as telematics service providers ("TSPs") 103, that collect driving data 105 from individual vehicles or fleets of vehicles. Alternatively, TSPs 103 may be third party providers of telemetric data, which is generated by units supplied by such providers,”).
Regarding claim 8 Carter teaches a non-transitory computer-readable storage medium for tangibly storing computer program instructions capable of being executed by a computer processor, the computer program instructions defining steps of:
receiving metrics associated with a vehicle (¶ 023); and
computing a driver score based on the driver update value, a previous score, and a learning rate (¶ 023).
Carter does not explicitly mention generating a deviation vector based on the metrics and a plurality of aggregated values corresponding to the metrics;
computing a driver update value based on the deviation vector and a plurality of model parameters, each of the plurality of model parameters corresponding to the metrics. Ameyoe from an analogous art teaches a method for detecting a change in conductor behavior uses a cybernetic model defined by a first measurement vector, an output vector for estimating one or more driver-generated data, a parameterization vector, and a temporal state vector of the model (Abstract) and the concepts of generating a deviation vector based on the metrics and a plurality of aggregated values corresponding to the metrics (Pg. 17 lines 6-9, Pg. 19, lines 20-27);
computing a driver update value based on the deviation vector and a plurality of model parameters, each of the plurality of model parameters corresponding to the metrics (Pg. 3, lines 19-29). Therefore, it would have been obvious for one of ordinary skill in the art at the time of filing the invention to combine the a non-transitory computer-readable storage medium in the driver monitoring and scoring system of Carter with generating a deviation vector based on the metrics and a plurality of aggregated values corresponding to the metrics; and computing a driver update value based on the deviation vector and a plurality of model parameters, each of the plurality of model parameters corresponding to the metrics, as taught by Ameyoe in order to calculate a covariance matrix on output values).
Regarding claim 9 Carter and Ameyoe teach the medium of claim 8, and Carter teaches the computer program instructions defining the step of: generating the aggregated values by:
receiving, for a plurality of road segments, corresponding metrics from a plurality of drivers; and
aggregating, for each of the plurality of road segments, the corresponding metrics.
Regarding claim 10 Carter and Ameyoe teach the medium of claim 9, and Carter teaches wherein aggregating the corresponding metrics further comprises averaging the corresponding metrics (¶ 045).
Regarding claim 13 Carter and Ameyoe teach the medium of claim 8, and Carter teaches the computer program instructions defining the step of calculating the model parameters via a statistical learning methodology (¶ 023).
Regarding claim 14 Carter and Ameyoe teach the medium of claim 13, and Carter teaches wherein the statistical learning methodology is trained using a combination of video, telematics, and externally-obtained data (¶ 026, ¶ 043).
Regarding claim 15 Carter teaches a device comprising:
a processor configured to:
receive metrics associated with a vehicle (¶ 023); and
compute a driver score based on the driver update value, a previous score, and a learning rate (¶ 023).
Carter does not explicitly mention generating a deviation vector based on the metrics and a plurality of aggregated values corresponding to the metrics;
computing a driver update value based on the deviation vector and a plurality of model parameters, each of the plurality of model parameters corresponding to the metrics. Ameyoe from an analogous art teaches a method for detecting a change in conductor behavior uses a cybernetic model defined by a first measurement vector, an output vector for estimating one or more driver-generated data, a parameterization vector, and a temporal state vector of the model (Abstract) and the concepts of generating a deviation vector based on the metrics and a plurality of aggregated values corresponding to the metrics (Pg. 17 lines 6-9, Pg. 19, lines 20-27);
computing a driver update value based on the deviation vector and a plurality of model parameters, each of the plurality of model parameters corresponding to the metrics (Pg. 3, lines 19-29). Therefore, it would have been obvious for one of ordinary skill in the art at the time of filing the invention to combine the device comprising a processor in the driver monitoring and scoring system of Carter with generating a deviation vector based on the metrics and a plurality of aggregated values corresponding to the metrics; and computing a driver update value based on the deviation vector and a plurality of model parameters, each of the plurality of model parameters corresponding to the metrics, as taught by Ameyoe in order to calculate a covariance matrix on output values.
Regarding claim 16 Carter and Ameyoe teach the device of claim 15, and Carter teaches the processor further configured to generate the aggregated values by:
receiving, for a plurality of road segments, corresponding metrics from a plurality of drivers (¶ 004); and
aggregating, for each of the plurality of road segments, the corresponding metrics (¶ 093).
Regarding claim 17 Carter and Ameyoe teach the device of claim 16, and Carter teaches wherein aggregating the corresponding metrics further comprises averaging the corresponding metrics (¶ 045).
Regarding claim 20 Carter and Ameyoe teach the device of claim 15, and Carter teaches the processor further configured to calculate the model parameters via a statistical learning methodology (¶ 023).
Claim Rejections - 35 USC § 103
Claims 4-5, 11-12 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Carver et al. (U.S. Patent Application Pub. 2015/0294422) in view of Ameyoe et al. (FR 3032919) and further in view of Paromtchik et al. (U.S. Patent Application Pub. 2016/0035220).
Regarding claim 4 Carter and Ameyoe teach the method of claim 2, but both are silent on wherein computing a driver update value based on the deviation vector comprises computing deviation values for each of the metrics, the deviation value computed by subtracting a corresponding aggregated value from the corresponding metric. Paromtchik from an analogous driver monitoring and scoring art teaches the concept wherein computing a driver update value based on the deviation vector comprises computing deviation values for each of the metrics, the deviation value computed by subtracting a corresponding aggregated value from the corresponding metric (¶ 074; "A set of deviation values |.DELTA.Q(t.sub.k,p)|=|P(t.sub.k,p)-Q(t.sub.k,p)] representative of the deviation of the actual path travelled by the vehicle from the
estimated reference path P(t), t.dl-elect cons.[T.sub.k,T.sub.k+1] is next computed at step 46, for each p between 1 and n. The difference .DELTA.Q(t.sub.k,p)=P(Lsub.k,p)}-Q(t.sub.k,p) Is a two-dimensional vector, and a corresponding deviation value .DELTA.Q(t.sub.k,p) is for example the L2 norm of said vector,”). Therefore it would have been obvious for one of ordinary skill in the art at the time of filing the invention to combine the driver monitoring and scoring system of Carter with the support for subtracting metrics to determine a deviation value of Paromtchik because such systems and methods allow for determining deviation data by comparing expected metrics with observed metrics.
Regarding claim 5 Carter, Ameyoe and Paromtchik teach the method of claim 4, and Paromtchik further teaches wherein computing deviation values for each of the metrics comprises: selecting a plurality of road segments (¶ 044; “The positions obtained from measurements show the deviation of each vehicle as compared to the respective travelled segments of reference paths 12, 14,"); computing deviation values for the metric for each of the plurality of road segments (¶ 044; ¶ 074); and summing the deviations values to generate the deviation value for the metric (¶ 045; "These Indices are then summed for a common group of trips, or period of time using a ime weighted average (proportional to exposure) to produce the fleet level score stored, along with the Individual scores, by VIN number In the results database 101, again using a temporal database structure for storing and summing these elements, to bias the results to the most recent events, or time series data because It has been shown that recent events are much more predictive of risk factors than historical patterns when the pattem sequence Is changing,”). The motivation is the same as claim 4.
Regarding claim 11 Carter and Ameyoe teach the medium of claim 9, but both are silent on wherein computing a driver update value based on the deviation vector comprises computing deviation values for each of the metrics, the deviation value computed by subtracting a corresponding aggregated value from the corresponding metric. Paromtchik from an analogous driver monitoring and scoring art teaches the concept wherein computing a driver update value based on the deviation vector comprises computing deviation values for each of the metrics, the deviation value computed by subtracting a corresponding aggregated value from the corresponding metric (¶ 074; "A set of deviation values |.DELTA.Q(t.sub.k,p)|=|P(t.sub.k,p)-Q(t.sub.k,p)] representative of the deviation of the actual path travelled by the vehicle from the
estimated reference path P(t), t.dl-elect cons.[T.sub.k,T.sub.k+1] is next computed at step 46, for each p between 1 and n. The difference .DELTA.Q(t.sub.k,p)=P(Lsub.k,p)}-Q(t.sub.k,p) Is a two-dimensional vector, and a corresponding deviation value .DELTA.Q(t.sub.k,p) is for example the L2 norm of said vector,”). Therefore it would have been obvious for one of ordinary skill in the art at the time of filing the invention to combine the driver monitoring and scoring medium of Carter with the support for subtracting metrics to determine a deviation value of Paromtchik because such systems and methods allow for determining deviation data by comparing expected metrics with observed metrics.
Regarding claim 12 Carter, Ameyoe and Paromtchik teach the medium of claim 11, and Paromtchik further teaches wherein computing deviation values for each of the metrics comprises:
selecting a plurality of road segments (¶ 044; “The positions obtained from measurements show the deviation of each vehicle as compared to the respective travelled segments of reference paths 12, 14,");
computing deviation values for the metric for each of the plurality of road segments (¶ 044; ¶ 074); and
summing the deviations values to generate the deviation value for the metric (¶ 045; "These Indices are then summed for a common group of trips, or period of time using a time weighted average (proportional to exposure) to produce the fleet level score stored, along with the Individual scores, by VIN number In the results database 101, again using a temporal database structure for storing and summing these elements, to bias the results to the most recent events, or time series data because It has been shown that recent events are much more predictive of risk factors than historical patterns when the pattern sequence Is changing,”). The motivation is the same as claim 11.
.
Regarding claim 18 Carter and Ameyoe teach the medium of claim 16, but both are silent on wherein computing a driver update value based on the deviation vector comprises computing deviation values for each of the metrics, the deviation value computed by subtracting a corresponding aggregated value from the corresponding metric. Paromtchik from an analogous driver monitoring and scoring art teaches the concept wherein computing a driver update value based on the deviation vector comprises computing deviation values for each of the metrics, the deviation value computed by subtracting a corresponding aggregated value from the corresponding metric (¶ 074; "A set of deviation values |.DELTA.Q(t.sub.k,p)|=|P(t.sub.k,p)-Q(t.sub.k,p)] representative of the deviation of the actual path travelled by the vehicle from the estimated reference path P(t), t.dl-elect cons.[T.sub.k,T.sub.k+1] is next computed at step 46, for each p between 1 and n. The difference .DELTA.Q(t.sub.k,p)=P(Lsub.k,p)}-Q(t.sub.k,p) Is a two-dimensional vector, and a corresponding deviation value .DELTA.Q(t.sub.k,p) is for example the L2 norm of said vector,”). Therefore it would have been obvious for one of ordinary skill in the art at the time of filing the invention to combine the driver monitoring and scoring device of Carter with the support for subtracting metrics to determine a deviation value of Paromtchik because such systems and methods allow for determining deviation data by comparing expected metrics with observed metrics.
Regarding claim 19 Carter, Ameyoe and Paromtchik teach the device of claim 18, and Paromtchik further teaches wherein computing deviation values for each of the metrics comprises:
selecting a plurality of road segments (¶ 044; “The positions obtained from measurements show the deviation of each vehicle as compared to the respective travelled segments of reference paths 12, 14,");
computing deviation values for the metric for each of the plurality of road segments (¶ 044; ¶ 074); and
summing the deviations values to generate the deviation value for the metric (¶ 045; "These Indices are then summed for a common group of trips, or period of time using a time weighted average (proportional to exposure) to produce the fleet level score stored, along with the Individual scores, by VIN number In the results database 101, again using a temporal database structure for storing and summing these elements, to bias the results to the most recent events, or time series data because It has been shown that recent events are much more predictive of risk factors than historical patterns when the pattern sequence Is changing,”). The motivation is the same as claim 18.
Response to Arguments
Applicant's arguments filed 10/03/2025 have been fully considered but they are not persuasive.
Applicant’s Arguments:
(1) (Remarks, filed 10/03/2025, pg. 7, para. 5 – pg. 8 para. 3)
In rejecting claims 1, 8 and 15, Applicant argues that Ameyoe does not disclose, teach or suggest " generating a deviation vector that represents differences between a driver's metrics and aggregated values from multiple drivers on the same road segments".
Examiner argues that the claim does not recite any verbage or include any context whereby the “aggregated values” are “from multiple drivers on a same road segment” is required.
In response to Applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “aggregated values are from multiple drivers on a same road segments, computing a numerical driver update value that contributes to score calculation, or ) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
(2) (Remarks, filed 10/03/2025, pg. 8, para. 5)
Applicant argues that Ameyoe does not describe computing any "driver update value" from these parameters.
Examiner respectfully disagrees. Examiner posits that Applicant admits in the same paragraph that Ameyoe evaluates parameter stability over time to detect behavior changes and outputs a binary signal on parameters regarding metrics when instability indicates a change in driver behavior. The current claim language makes no mention of computing a numerical driver update value that contributes to score calculation nor numerical score calculations. Also, the driver responding to inputs like steering torque and road curvature whereby the binary signal is generated therefrom can act as a driver update value or score, whether it be numerical, PASS or FAIL or in other alpha-numeric form via the binary generation.
In response to Applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “computing a numerical driver update value) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
(3) (Remarks, filed 10/03/2025, pg. 9, para. 3)
Applicant argues that Ameyoe does not describe the computing including "a learning rate" and that the concept of a learning rate, which controls how quickly new information influences an evolving score, is entirely absent from Ameyoe.
Examiner respectfully disagrees. Examiner interprets Ameyoe’s use of temporal analysis when considering recent parameter values as being equivalent to a learning rate because the broad language of the claim does not in any way define the “learning rate”. Moreover, the claim also does not describe or include any specific language/detail regarding any mechanism for balancing new measurements against historical scores through a learning rate parameter, as argued.
In response to Applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., the concept of a learning rate, which controls how quickly new information influences an evolving score) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
(4) (Remarks, filed 10/03/2025, pg. 9, para. 3)
The claims require a specific mathematical framework where the learning rate mediates between the driver update value and previous score to compute a new score
Examiner respectfully disagrees. In response to Applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., a specific mathematical framework where the learning rate mediates between the driver update value and previous score to compute a new score) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
(5) (Remarks, filed 10/03/2025, pg. 9, para. 3)
The claims describe a scoring system that benchmarks drivers against road segment norms. This requires first establishing baselines from aggregated driver data for each road segment, then measuring how individual drivers deviate from these baselines, and finally using these deviations with model parameters to update driver scores through a learning rate mechanism. This comparative scoring approach, which evaluates drivers relative to segment-specific norms rather than their own temporal stability, represents a different technical solution to a different problem.
Examiner respectfully disagrees. In response to Applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., a scoring system that benchmarks drivers against road segment norms) is not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
(6) (Remarks, filed 10/03/2025, pg. 10, para. 2)
The Office Action suggests combining these references "in order to calculate a covariance matrix on output values," but this rationale does not explain why one skilled in the art would modify either system to implement the specific claimed elements.
In response to Applicant's argument that combining the references "in order to calculate a covariance matrix on output values, the fact that the inventor has recognized another advantage which would flow naturally from following the suggestion of the prior art cannot be the basis for patentability when the differences would otherwise be obvious. See Ex parte Obiaya, 227 USPQ 58, 60 (Bd. Pat. App. & Inter. 1985).
In response to applicant' s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may 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 one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, the motivation to do so is found in the knowledge generally available to one of ordinary skill in the art.
[End of Arguments].
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MANCIL H LITTLEJOHN JR whose telephone number is (571)270-3718. The examiner can normally be reached M-F 8:30-5 (CST).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Quan-Zhen Wang can be reached on (571) 272-3114. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MANCIL LITTLEJOHN JR/Examiner, Art Unit 2685
/QUAN ZHEN WANG/ Supervisory Patent Examiner, Art Unit 2685