DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment filed on 01/21/2026 have been entered. Claims 1-4 and 6-20 remain pending in the application. Claim 5 is cancelled. Applicant’s amendments to the claims have overcome the 101 rejection set forth in the previous Non-Final Office Action.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 17 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claim 17 was amended to include that “the labeled time delay is determined as the lag corresponding to the extremum of a normalized cross-correlation function”. Applicant’s disclosure does not provide support for the normalization of the cross-correlation function. The only mention of a “normalizing” operation in applicant’s disclosure is in reference to techniques that are applied to the layers of the ML model, and not the cross-correlation function itself ([0065]). It is recommended that the term “normalized” be removed from the claim.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4, 6, and 8-14 are rejected under 35 U.S.C. 103 as being unpatentable over Bakke et al. (US 20160066505 A1) in view of Thompson et al. (US 20120029754 A1).
Regarding claim 1. Bakke teaches a computer-implemented method, comprising:
accessing, from a first sensor of a harvester, first sensor data comprising a first time-stamped signal indicative of a first parameter associated with crop harvesting performed within a field ([0093] and [0136], where a plurality of sensor readings are saved in storage);
accessing, from a second sensor of the harvester, second sensor data comprising a second time-stamped signal indicative of a second parameter associated with the crop harvesting performed within the field ([0093] and [0136], where a plurality of sensor readings are saved in storage);
determining, as a time shift between the first sensor data and the second sensor data, a lag corresponding to a maximum likelihood of correlation ([0336], [0340], and Fig. 25B, where data points of the first and second data corresponding to a common location area are determined)
and applying a time shift to the first sensor data to time align the first sensor data with the second sensor data and generate a time-invariant data set ([0334-0335] and see Fig. 25B, where the harvest data records are adjusted so that the correlated data points align with the same area).
Bakke teaches generating, based on at least the second sensor data, a prescription map indicative of a plan for managing the field ([0332] and [0349-0350], where the torque levels of a combine are associated with harvest yield data and the correlation is sent to update historical harvest data; [0076], [0116], and [0131], where the prescriptions are generated off historical harvest yield data and sensor data). However, Bakke does not explicitly teach that this generating is also based on the time-invariant data set.
Bakke additionally teaches that the agricultural prescription is updated based on all yield data ([0326]), further teaching that the time-invariant data and time adjustments determined are used to perform this updating ([0333]). It also teaches that the harvest data includes historical summaries, including calibrations of previous augur data and actual crop yield data. ([0322-0333]). This calibration includes adjusting the data to produce a time-invariant dataset ([0334]).
Therefore, it would have been obvious to a skilled artisan to have the generated agricultural prescriptions also be based on the generated time-invariant harvest yield data produced as a result of the time adjustment factor for the motivation, as taught by Bakke, of improving the accuracy of crop harvesting sensor data ([0326]) by “account[ing] for delays within harvesting equipment” ([0334]).
Bakke teaches that data points of the first and second signal that “maximize a likelihood of correlation to a common location area” are determined ([0336]). However, it does not teach that these points are calculated by computing a cross-correlation function between the first time-stamped signal and the second time-stamped signal, as a function of lag of the first time-stamped signal relative to the second time-stamped signal.
Various cross-correlation functions are known in the art. In the same field of endeavor, Thompson teaches one such cross-correlation function for calculating time lag between two data signals in an agricultural system, said system performing operations including:
computing a cross-correlation function between the first time-stamped signal and the second time-stamped signal, as a function of lag of the first time-stamped signal relative to the second time-stamped signal ([0027], where “the cross correlation function may provide the time lag difference” between the two signals).
It would have been obvious to one of ordinary skill in the art at the effective date of filing to modify Bakke to use the cross-correlation function of Thompson based on a reasonable expectation of success and motivation of accurately determining which two points of the signals of Bakke are indeed correlated, ensuring that any subsequent operations are performed on more accurately correlated data.
Regarding claim 2, the prior art remains as applied in claim 1. Bakke teaches:
wherein the first sensor data comprises a mass flow rate associated with the harvester ([0136], where the sensor is a yield monitor in the form of a capacitive flow sensor),
and the second sensor data comprises an engine torque associated with the harvester ([0359], where the sensor data includes “a combine torque level”).
Regarding claim 3, the prior art remains as applied in claim 2. Bakke teaches
accessing a plot of mass flow sensor data versus satellite positioning system data indicative of a latitude and longitude of the field ([0230], where sensor data is associated with a transversal using GPS coordinates);
based at least on the second sensor data and the time-invariant data set, updating the plot to generate an updated plot ([0313] and [0334], where the harvest data is updated and used to generate a map);
and communicating the updated plot to a display device associated with the harvester ([0313]).
Regarding claim 4, the prior art remains as applied in claim 1. Bakke teaches:
wherein the time shift that is applied is done as a function of a lag of the first time-stamped signal relative to the second time stamped signal ([0334], where the delay between ingestion of a crop for harvesting and where a particular metric of the harvest data is applied is determined and used to correct sensor data measurements).
Thompson further teaches:
wherein cross- correlating the first time-stamped signal and the second time-stamped signal comprises measuring a similarity of the first time-stamped signal and the second time-stamped signal ([0027], where the similarities between the amplitudes of the two signals is used to perform the cross-correlating).
Regarding claim 6, the prior art remains as applied in claim 1. Bakke teaches:
wherein the time shift is based on at least one of: an average over a time period or a moving average over a subset of the time period ([0101], where the sensor data used to determine the time shift is captured as an “average sample”, which must be taken over a time period).
Regarding claim 8, the prior at remains as applied in claim 8. Bakke teaches:
wherein the plan for managing the field comprises an input to a farming tool to control application of fertilizer, herbicide, or seeds to the field ([0150], where the agricultural prescription parameters are used for controlling planting cycles, fertilizing cycles, and weeding plans),
wherein the plan is updated based on the timeinvariant data set ([0319] and [0326], where the prescription plan is updated).
Regarding claim 9, the prior art remains as applied in claim 1. Bakke teaches
wherein the method is performed as post-processing operations associated with the harvester ([0076] and [0150], where the method is performed after operation in order to update future prescription plans).
Regarding claim 10, Bakke teaches a computerized system, comprising:
at least one computer processor ([0089]);
and computer memory storing computer-useable instructions ([0090]) that, when used by at least one computer processor, cause the at least one computer processor to perform operations comprising:
accessing, from a first sensor of a harvester, first sensor data comprising a first time-stamped signal indicative of a first parameter associated with crop harvesting performed within a field ([0093] and [0136], where a plurality of sensor readings are saved in storage);
accessing, from a second sensor of the harvester, second sensor data comprising a second time-stamped signal indicative of a second parameter associated with the crop harvesting performed within the field ([0093] and [0136], where a plurality of sensor readings are saved in storage);
determining, as a time shift between the first sensor data and the second sensor data, a lag corresponding to a maximum likelihood of correlation ([0336], [0340], and Fig. 25B, where data points of the first and second data corresponding to a common location area are determined)
and applying a time shift to the first sensor data to time align the first sensor data with the second sensor data and generate a time-invariant data set ([0334-0335] and see Fig. 25B, where the harvest data records are adjusted so that the correlated data points align with the same area).
Bakke teaches generating a graphical user interface (GUI) comprising a plot indicative of at least the second sensor data plotted against satellite positioning system data ([0332] and [0349-0350], where the torque levels of a combine are associated with harvest yield data and the correlation is sent to update historical harvest data; [0076], [0116], and [0131], where the prescriptions are generated off historical harvest yield data and sensor data; [035], where the prescriptions and data are generated as GUIs in the form of maps). However, Bakke does not explicitly teach that this generating is also based on the time-invariant data set.
Bakke additionally teaches that the agricultural prescription is updated based on all yield data ([0326]), further teaching that the time-invariant data and time adjustments determined are used to perform this updating ([0333]). It also teaches that the harvest data includes historical summaries, including calibrations of previous augur data and actual crop yield data. ([0322-0333]). This calibration includes adjusting the data to produce a time-invariant dataset ([0334]).
Therefore, it would have been obvious to a skilled artisan to have the generated agricultural prescriptions also be based on the generated time-invariant harvest yield data produced as a result of the time adjustment factor for the motivation, as taught by Bakke, of improving the accuracy of crop harvesting sensor data ([0326]) by “account[ing] for delays within harvesting equipment” ([0334]).
Bakke teaches that data points of the first and second signal that “maximize a likelihood of correlation to a common location area” are determined ([0336]). However, it does not teach that these points are calculated by computing a cross-correlation function between the first time-stamped signal and the second time-stamped signal, as a function of lag of the first time-stamped signal relative to the second time-stamped signal.
Various cross-correlation functions are known in the art. In the same field of endeavor, Thompson teaches one such cross-correlation function for calculating time lag between two data signals in an agricultural system, said system performing operations including:
computing a cross-correlation function between the first time-stamped signal and the second time-stamped signal, as a function of lag of the first time-stamped signal relative to the second time-stamped signal ([0027], where “the cross correlation function may provide the time lag difference” between the two signals).
It would have been obvious to one of ordinary skill in the art at the effective date of filing to modify Bakke to use the cross-correlation function of Thompson based on a reasonable expectation of success and motivation of accurately determining which two points of the signals of Bakke are indeed correlated, ensuring that any subsequent operations are performed on more accurately correlated data.
Regarding claim 11, the prior art remains as applied in claim 10. Bakke teaches:
wherein the GUI's generated on a display of the harvester ([0313], where the data is sent to a user device 1-1A for visualization; [0060] and [0082] and see Figs. 1 and 4, where the user-device 1-1A is the agricultural machine with a cabin and a user interface output device in the form of a display).
While Bakke does not explicitly teach that the display of the harvester is within the cabin, displays within the cabin area of farm machinery vehicles are well known, and it would have been obvious to one of ordinary skill that the display of Bakke is within the cabin of the farm machinery so that an operator of the farm machinery has access to the generated GUI display while they operate the machinery in the field.
Regarding claim 12, the prior art remains as applied in claim 10. Bakke teaches:
wherein the first sensor data comprises a mass flow rate associated with the harvester ([0136], where the sensor is a yield monitor in the form of a capacitive flow sensor),
and the second sensor data comprises an engine torque associated with the harvester ([0359], where the sensor data includes “a combine torque level”), and wherein the operations comprise:
accessing a plot of mass flow sensor data versus satellite positioning system data indicative of a latitude and longitude of the field ([0230], where sensor data is associated with a transversal using GPS coordinates);
based at least on the second sensor data and the time-invariant data set, updating the plot to generate an updated plot ([0313] and [0334], where the harvest data is updated and used to generate a map);
and communicating the updated plot to a display device associated with the harvester ([0313]).
Regarding claim 13, the prior art remains as applied in claim 10. Bakke teaches:
wherein the time shift comprises the lag of the first time-stamped signal relative to the second time-stamped signal. ([0334], where the delay between ingestion of a crop for harvesting and where a particular metric of the harvest data is applied is determined and used to correct sensor data measurements).
Thompson further teaches:
wherein cross- correlating the first time-stamped signal and the second time-stamped signal comprises measuring a similarity of the first time-stamped signal and the second time-stamped signal ([0027], where the similarities between the amplitudes of the two signals is used to perform the cross-correlating).
Regarding claim 14, the prior art remains as applied in claim 10. Bakke teaches:
wherein the harvester comprises a work vehicle mechanically coupled to a harvester assembly ([0136]),
wherein the first sensor is positioned on the harvester assembly ([0136], where the first sensor is a yield monitor positioned on the harvesting assembly equipment).
Although Bakke does not explicitly that the second sensor is positioned on a work vehicle of the harvester, as the second sensor can be an engine sensor ([0136], and the second sensor data includes a combine torque level ([0359]), it would have been obvious to the skilled artisan that the second sensor is positioned on a work vehicle of the harvester in order to obtain the engine speed and combine torque data as required.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Bakke in view of Thompson as applied to claim 1 above, and further in view of Wibbeke et al. (NPL 'Estimating time-delayed variables using transformer-based soft sensors' as disclosed by the applicant).
Regarding claim 7, the prior art remains as applied in claim 1. The prior combination does not teach wherein the first time-stamped signal and the second time-stamped signal are cross-correlated over a time period of at least 500 seconds.
Pertinent to the problem of estimating time-delayed variables, Wibbeke teaches training models to predict time-delayed variable values for agricultural operations. These models are analyzed over a set of data wherein the first time-stamped signal and the second time-stamped signal are cross-correlated over a time period of at least 500 seconds (page 6, section “Data Sets”, where the dataset inputted to a model to calculate time-delayed variable values is cross-correlated over 100,000 samples taken with a sampling rate of .1 or .2 Hz, which is 1,000,000 or 500,000 seconds depending on the dataset chosen).
It would have been obvious to one of ordinary skill in the art at the effective date of filing to modify the prior combination to ensure that the time period of sampling is at least 500 seconds based on a reasonable expectation of success and motivation of ensuring that the dataset is sufficiently large in order to produce an accurate time delay. Harvesters are well known in the art to be slow traveling, often taking days to harvest a field. Bakke additionally performs its determination of the time delay via analyzing adjacent transversals ([0334]). As a result, a small dataset being generated as a result of a data gathering period being too short would result in a first signal being cross-correlated with an incorrect second signal that does not accurately reflect the time delay. Therefore, ensuring that the data is of a sufficiently large time period confirms that subsequent cross-correlating will be accurate.
Claims 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Bakke in view of Thompson et al. (US 20120029754 A1) and Sidon et al. (US 20210321567 A1).
Regarding claim 15, Bakke teaches:
at least one computer-storage media having computer- executable instructions embodied thereon ([0089-0090]) that, when executed by a computing system having a processor and memory, cause the processor to perform operations comprising:
accessing first sensor data from a first sensor of a harvester and second sensor data from a second sensor of the harvester ([0093] and [0136], where a plurality of sensor readings are saved in storage);
extracting from the first sensor data a first feature indicative of a first parameter associated with crop harvesting performed within a field and from the second sensor data a second feature indicative of a second parameter associated with the crop harvesting performed within the field ([0136] and [0359], where raw values are obtained from sensors and parameters associated with crop harvesting, such as yield, combine torque, etc., are extracted from these sensors);
and applying the time delay as a time shift to the first sensor data to time-align the first sensor data with the second sensor data and generate a time-invariant data set ([0334] and see Fig. 25B, where the harvest data records are adjusted based on a time of ingestion of crop).
Bakke teaches, based on at least the second sensor data, causing a prescription map indicative of a plan for managing the field to be generated ([0332] and [0349-0350], where the torque levels of a combine are associated with harvest yield data and the correlation is sent to update historical harvest data; [0076], [0116], and [0131], where the prescriptions are generated off historical harvest yield data and sensor data). However, Bakke does not explicitly teach that this generating is also based on the time-invariant data set.
Bakke additionally teaches that the agricultural prescription is updated based on all yield data ([0326]), further teaching that the time-invariant data and time adjustments determined are used to perform this updating ([0333]). It also teaches that the harvest data includes historical summaries, including calibrations of previous augur data and actual crop yield data. ([0322-0333]). This calibration includes adjusting the data to produce a time-invariant dataset ([0334]).
Therefore, it would have been obvious to a skilled artisan to have the generated agricultural prescriptions also be based on the generated time-invariant harvest yield data produced as a result of the time adjustment factor for the motivation, as taught by Bakke, of improving the accuracy of crop harvesting sensor data ([0326]) by “account[ing] for delays within harvesting equipment” ([0334]).
Bakke teaches that the time delay is equal to a lag calculated by analyzing where two data points between the first sensor data and the second sensor data attain an extremum ([0334-0336], where the two data points are identified “that maximizes a likelihood of correlation to a common location area”). However, Bakke does not teach that this likelihood is calculated with a cross-correlation function.
Various cross-correlation functions are known in the art. In the same field of endeavor, Thompson teaches one such cross-correlation function for calculating time lag between two data signals in an agricultural system ([0027], where “the cross correlation function may provide the time lag difference” between the two signals).
It would have been obvious to one of ordinary skill in the art at the effective date of filing to modify Bakke to use the cross-correlation function of Thompson based on a reasonable expectation of success and motivation of accurately determining which two points of the signals of Bakke are indeed correlated, ensuring that any subsequent operations are performed on more accurately correlated data.
Bakke does not teach that the features extracted from the sensor data are machine learning (ML) features, and does not teach, based on the first ML feature and the second ML feature, determining, via a time-delay ML model, a time delay between the first sensor data and the second sensor data; wherein the time-delay ML model is trained using training examples each including first sensor training data, second sensor training data, and a labeled time delay.
In the same field of endeavor, Sidon teaches a system of using machine learning to produce accurate yield data. The operations of said system comprise:
extracting machine learning (ML) features from a first and second sensor data ([0058-0059], where the parameters are measured and/or extracted from sensor data);
based on the first ML feature and the second ML feature, determining, via a time-delay ML model, a time delay between the first sensor data and the second sensor data ([0058-0059], where the parameters are fed into a model that is used to calculate a time delay to apply so as to increase the accuracy of a predicted yield);
wherein the time-delay ML model is trained using training examples each including first sensor training data and second sensor training data ([0084-0086], where the ML mode is trained with first and second sensor training data).
A skilled artisan would have been able to modify Bakke to use a ML model in the determination of the time delay. Bakke additionally discloses that the labeled time delay is sent as feedback to correct subsequent records ([0344]), and the ML models listed by Sidon, such as the convolution neural network, are required to be trained with labeled input and output data pairs in order for the model to be able to predict an output value when inputted with input values ([0070]). Therefore, it is implicit that labeled time delays are used to train the machine learning model as the model would otherwise be unable to predict and/or determine a time delay if it was not trained using labeled sensor data and time delay pairs.
It would have been obvious to one of ordinary skill in the art at the effective date of filing to modify Bakke with these ML operations based on a reasonable expectation of success and motivation, as taught by Sidon, of determining the variable delays in a harvesting machine as the delays and values that determine said delays are subject to change during the harvesting operation, and thus using the machine learning techniques disclosed will increase the calculation accuracy of said delays ([0022-0024]). The benefits of use of ML models are well known in the art, and modifying Sidon to use such an ML model would have been obvious in order to realize these well-known advantages.
Regarding claim 16, the prior art remains as applied in claim 15. Sidon teaches:
wherein the time-delay ML model comprises a plurality of layers that extract and compare features, and that output a prediction ([0070], where the machine learning algorithms included, such as the convolutional neural network, definitionally comprise a plurality of layers that perform the functionality as claimed).
Regarding claim 17, the prior art remains as applied in claim 15. Thompson teaches:
the labeled time delay is determined as the lag corresponding to the extremum of a normalized cross-correlation function between the first sensor training data and the second sensor training data ([0027], where the time difference in maximum amplitudes is determined).
Regarding claim 18, the prior art remains as applied in claim 17. Bakke teaches:
wherein the first sensor data comprises a mass flow rate associated with the harvester ([0136]),
and the second sensor data comprises an engine torque associated with the harvester ([0359]).
Sidon further teaches:
the first sensor training data comprises a prior mass flow rate ([0036] and [0041], where the first sensor training data includes previous crop yield and biomass),
and the second sensor training data comprises a prior data value ([0036] and [0041], where the previous data can ”include other items as well”; see Fig. 3).
Although the second sensor training data of Sidon is not taught to be engine torque, this is taught by the primary Bakke. Therefore, when combining the machine learning techniques taught by Sidon into the invention of Bakke, it would have been obvious to the skilled artisan that the second sensor training data would comprise the prior engine torque as a machine learning model is unable to produce accurate predictions if it has new categories of values input during its predictions that it was otherwise not trained on.
Regarding claim 19, the prior art remains as applied in claim 15. Bakke teaches:
wherein the first ML feature comprises an indication of a mass flow rate that is consumed by the time-delay ML model ([0136], where the first feature that is inputted into the machine-learning model is indicative of a mass flow rate as it is obtained from a yield flow sensor),
and the second ML feature comprises an indication of an engine torque that is consumed by the time-delay ML model ([0359], where the second feature is indicative of a combine torque level and is inputted into the machine-learning model).
Note that these features are consumed by the time-delay ML model when the time-delay ML model of the prior combination with Sidon uses them to produce an output value as is part of the well-known operations of ML models.
Regarding claim 20, the prior art remains as applied in claim 15. Bakke teaches:
wherein the prescription map defines operating parameters of the harvester at different geographic coordinates within the field ([0319], [0330], and Figs. 2-3, where the prescription map is for the unique geographic region in which the machine is operating).
Response to Arguments
Applicant’s arguments filed 1/21/2026 with respect to the rejections of claims 1-4 and 6-14 under 35 U.S.C 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made over Bakke in view of Thompson as necessitated by applicant’s amendments to the claims.
Applicant’s arguments filed 1/21/2026 with respect to the rejections of claims 15-20 under 35 U.S.C 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made over Bakke in view of Thompson and Sidon as necessitated by applicant’s amendments to the claims.
Regarding the amendment to claim 15, applicant argues against the previous rejection of the labeled time delays as were recognized as implicit to the teachings of Sidon, arguing that these training-label limitations are “specific and technical (tied to the disclosed signal-processing cross-correlation maximum/minimum used to compute time delay)”. This argument is unpersuasive. It is noted that combination of Bakke and Thompson are relied upon to teach the claimed “specific and technical” aspects of the time delay. When this combination is modified to include the ML model of Sidon, it is implicit that the model be trained with this labeled time delay as is the recognized fundamental principle of how ML models operate.
Conclusion
The following prior art made of record and not relied upon by the examiner is considered pertinent to applicant’s disclosure:
Blank (US 20210076569 A1) discloses a similar invention for managing the time delay of harvester yield values.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JACK R. BREWER whose telephone number is (571)272-4455. The examiner can normally be reached 10AM-6PM.
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/JACK R BREWER/Examiner, Art Unit 3663 /ADAM D TISSOT/Primary Examiner, Art Unit 3663
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