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 .
Introduction
The following is a final Office action in response to Applicant’s submission filed on 8/28/2025. Currently claims 1-13 are pending and claims 1, 12, 13 are independent. Claims 1, 8, 9, 11 have been amended from the original claim set dated 8/13/2023. No claims have been added or cancelled.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. EP22193500.0, filed on 9/1/2022.
Response to Amendments
Applicant’s amendments are acknowledged and necessitated the new grounds of rejection in this Office Action. In light of Applicant’s amendments, the 35 USC § 112(b) rejections are withdrawn.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/6/2025 and 1/28/2025 appears to be in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS is being considered by the Examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea), specifically an abstract idea, without significantly more. With respect to claims 1-13, following the guidance contained within MPEP 2106, the inquiry for patent eligibility follows two steps: Step 1: Does the claimed invention fall within one of the four statutory categories of invention? Step 2A (Prong 1): Is the claim “directed to” an abstract idea? Step 2A (Prong 2): Is the claim integrated into a practical application? Step 2B: Does the claim recite additional elements that amount to “significantly more” than the abstract idea?
In accordance with these steps, the Examiner finds the following:
Step 1: Claim 1 and its dependent claims (claims 2-11) are directed to a statutory category, namely a method. Claim 12 is directed to a statutory category, namely a method. Claim 13 is directed to a statutory category, namely system/machine.
Step 2A (Prong 1): Claims 1, 12, and 13, which are substantially similar claims to one another, are directed to the abstract idea of “Mental processes”, or more particularly, “Concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (See MPEP 2106).” In this application that refers to using a computer system evaluate and analyze repeatable delivery routines. To clarify this further, the Applicant’s disclosed invention is a conceptual system meant to perform the same function that an operations manager might perform for a delivery company or the US Post Office. The abstract elements of claims 1, 12, and 13, recite in part “Receive location data…Process data…Compare data…Identify number of POIs…Compare time information…Identify discrepancies…Compare timing of filtered POI…Train ML model…”. Dependent claims 2-11, add to the abstract idea the following limitations which recite in part “Repeatable processed comprise…POI comprises…Determine stop locations…Indicate discrepancies…Determine adjustments…Base adjustments on data…Data comprises GPS data…Indicate adjustments…”. All of these additional limitations, however, only serve to further limit the abstract idea, and hence are nonetheless directed towards fundamentally the same abstract idea as independent claims 1, 12, and 13.
Step 2A (Prong 2): Independent claims 1, 12, and 13, which are substantially similar claims to one another, do not contain additional elements, either considered individually or in combination, that effectively integrate the exception into a practical application of the exception. These claims do include the limitation that recites in part “Memory…Processors…Control system…Computer code…ML model…” which limits the claims to a networked/computer based environment, but this is insufficient with respect to integration into a practical application because it is merely applying the abstract idea to a general computer (See MPEP 2106.05(f)).
Dependent claims 5 and 11 add the additional element which recites in part “Device…GUI…” which again limits the claims to a networked/computer based environment, but this is again insufficient with respect to integration into a practical application because it is again merely applying the abstract idea to a general computer (See MPEP 2106.05(f)).
Additionally, dependent claims 2-4, 6-10 do not include any additional elements to conduct a further Step 2A (Prong 2) analysis.
Step 2B: Independent claims 1, 12, and 13, which are substantially similar claims to one another, include additional elements, when considered both individually and as an ordered combination, which are insufficient to amount to significantly more than the judicial exception. The additional elements of these claims recite in part “Memory…Processors…Control system…Computer code…ML model……”. These items are not significantly more because these are merely the software and/or hardware components used to implement the abstract idea (evaluate and analyze repeatable delivery routines) on a general purpose computer (See MPEP 2106.05(f)). This is exemplified in the Applicant’s specification in [0076] – “Such computer program instructions can be provided to a processor of a general purpose computer.”
Dependent claims 5 and 11 include additional elements, when considered both individually and as an ordered combination and in view of their respective independent claims, which are insufficient to amount to significantly more than the judicial exception. Specifically, dependent claims 5 and 11 include the additional element which recites in part “Device…GUI…” These are the same/similar additional elements that are addressed above in claims 1, 12, and 13, and are not significantly more because these are again merely the software and/or hardware components used to implement the abstract idea (evaluate and analyze repeatable delivery routines) on a general purpose computer (See MPEP 2106.05(f)).
Additionally, dependent claims 2-4, 6-10 do not include any additional elements to conduct a further 2B analysis.
Accordingly, whether taken individually or as an ordered combination claims 1-13 are rejected under 35 USC § 101 because the claimed invention is directed to a judicial exception, an abstract idea, without significantly more.
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, 3, 4, 5, 8, 9, 10, 11, 13 are rejected under 35 U.S.C. 103 as being unpatentable over Mouli et al. (US 20210182787 A1) in view of Turner et al. (US 20200334637 A1)
Regarding claims 1 and 13, Mouli discloses a computer-implemented method for indicating time discrepancies in a repeatable process performed by a plurality of vehicles (Mouli ABS - Systems and methods for analyzing and improving delivery resource operation and efficiency), the method comprising: receiving location data comprising a plurality of data points, each data point corresponding to a time-marked geographical position reported by a vehicle (Mouli ¶67 - At block 610, period location data is received. For example, the server 110 may receive the periodic location data transmitted by a mobile computing device 140. The periodic location data includes a number of location data points generated at the mobile computing device 140 on a periodic basis. For example, the mobile computing device 140 may generate a location data point based on GPS or other location technology, every 0.1 second, every 0.5 second, every second, every five seconds, every ten seconds, or longer, or at any other suitable interval. In some embodiments, the periodic location data is breadcrumb data collected at a constant interval to provide a record of a delivery resource's movement while servicing a delivery route); processing the location data to determine at least one data point subset which is indicative of a candidate point-of-interest, POI for the repeatable process (Mouli ¶69 - At block 620, the periodic location data is segmented into events and route segments for the delivery route based at least in part on the route information. For example, breadcrumb data collected at a mobile computing device 140 can be segmented into route segments and events as shown in FIG. 2, based on geofence data associated with the events and route segments); comparing the determined data point subset with one or more data point subsets associated with predetermined POls of a repeatable process (Mouli ¶56 -the server 110 can analyze the breadcrumb data for the out of bounds travel 276 to determine whether the out of bounds travel 276 occurs regularly for the carrier traversing the route, whether they are anomalies etc., by comparing a current days route information and delivery information with average or baseline route data, and/or with route and delivery data for a preset period, e.g., previous two weeks, a random selection of days, and the like); based on an output of the comparison: identifying a number of POls of the repeatable process (Mouli ¶46 - The delivery point section 264 includes columns identifying the types of delivery points along a route); comparing time information associated with one of the determined POls of the repeatable process with time information associated with the one or more predetermined Pols (Mouli ¶70 - At block 625, the segmented periodic location data is compared to the baseline information. In one example, the server 110 may compare the durations of the individual events and route segments of the segmented periodic location data to the stored baseline durations corresponding to the same events and route segments), and based on an output of the comparison of time information, identifying one or more time discrepancies in the repeatable process (Mouli ¶61 - The server 110 can compare the average times to the actual times for a given day and can identify any anomalies or excessive differences between the two).
Mouli lacks filtering the candidate POIs based on a confidence value derived from a frequency of occurrence over multiple vehicle trajectories, and wherein the time discrepancy is identified by comparing a timing of at least one filtered POI with a predefined timing window for a corresponding process step defined in an expected process model.
Turner, from the same field of endeavor, teaches filtering the candidate POIs based on a confidence value derived from a frequency of occurrence over multiple vehicle trajectories (Turner ¶120 - By such collection of regular, unplanned stops (regular in time, duration, location, etc.) the route 102 can be updated to account for the common or regular stops. In some embodiments, the unplanned stops are collected over some time period, for example, one week, two weeks, one month, two months, and so forth, identifying where clusters of the unplanned stops occur. Assuming that a cluster represents a history of stops at one location, a general location for the cluster of unplanned stopped can be determined (for example, by averaging the latitudes, longitudes, and headings of each of the stops in the cluster of unplanned stops). Furthermore, the density of each cluster may be deterministic of a frequency with which the delivery vehicle 110 stops at the corresponding location…The resulting clusters will likely represent frequent stops at a single location, as in FIG. 7. Setting the minimum number of points to a smaller value will enable the clustering algorithm to identify smaller clusters that may represent stops that are visited less frequently), and wherein the time discrepancy is identified by comparing a timing of at least one filtered POI with a predefined timing window for a corresponding process step defined in an expected process model (Turner Fig. 11B - Turner ¶210 - At block 1404, the system 900 may compare a nominal delivery route with the received digital route model… At block 1406, the system 900 may calculate a route evaluation based on the comparison between the nominal delivery route and the digital route model. In some embodiments, the route evaluation may comprise a percentage value identifying a difference between the nominal delivery route and the digital route model. The percentage value may be calculated based on a mileage difference between the nominal delivery route and the digital route model, a difference in time required to service the nominal delivery route as compared to the digital route model, and so forth).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the route analysis methodology/system of Mouli by including the delivery analysis techniques of Turner because Turner discloses “additional techniques of identifying and improving efficiencies of delivery routes (Turner ¶3)”. Additionally, Mouli further details that “This disclosure relates to methods and systems for mapping routes of delivery resources and improving delivery efficiency (Mouli ¶2)” so it would be obvious to consider including the additional delivery analysis techniques that Turner discloses because it would provide additional efficiency improvement of the routes disclosed within Mouli.
Regarding claim 3, Mouli in view of Turner discloses at least one POI comprises a vehicle stop location in the repeatable process (Mouli ¶20 - Understanding a carrier route means that accurate route information needs to be gathered. The route information includes delivery point information {POI}).
Regarding claim 4, Mouli in view of Turner discloses a plurality of vehicle stop locations are determined for the repeatable process, and the location data and time-information define at least one link between two of the plurality of vehicle stop locations (Mouli ¶29 - The route database 120 can store information for a plurality of routes, including information for each delivery point along each of the plurality of routes. The delivery point information can include an accurate geographic location for each delivery point, including, for example, a location for the front door, item receptacle, or other device for each delivery point).
Regarding claim 5, Mouli in view of Turner discloses indicating the identified one or more time discrepancies in the production via a graphical user interface, GUI provided on a display of an electronic device (Mouli Fig. 2, Fig. 5 – Mouli ¶45 - The server can analyze each duration, each start time, and/or each end time to determine whether any of the time entries exceed a threshold parameter, or if they look longer than expected. In some embodiments, the server 110 can compare the start, end and/or duration times for the date of the table 200 with start, end, and duration times from the baseline route information or the average route information. If a discrepancy, anomaly, or exception is identified, a notification, highlight, or other indicator can be generated. For example, in the time section 263 for the loading duration, the duration value is highlighted in a different color, for example, red, indicating that the loading duration is an anomaly or an exception).
Regarding claim 8, Mouli in view of Turner discloses recommended adjustments are at least in part based on the duration, timing, and location of stops in the repeatable process (Mouli ¶71 - At block 630, the baseline information is updated {i.e. adjusted}...In another example, the server 110 may adjust the delivery route itself, for example, by changing a driving direction based on a detected recurring deviation, shifting one or more delivery points to a different route or adding delivery points from a different route based on the delivery route repeatedly taking more or less time than expected).
Regarding claim 9, Mouli in view of Turner discloses recommended adjustments are at least in part based on the location data and timing information of more than one vehicle operating in the vicinity of the vehicle (Mouli ¶71 - At block 630, the baseline information is updated {i.e. adjusted}...The server 110 may schedule additional delivery resources {i.e. other vehicles} to service some or all of the delivery route on a permanent or temporary basis, for example, based on delays, changing route conditions, and the like).
Regarding claim 10, Mouli in view of Turner discloses the location data comprises a real-time GPS data feed for each vehicle of the plurality of vehicles (Mouli ¶21 - Location and activity information of the delivery resource can be obtained from breadcrumb data, such as GPS data, generated by a delivery resource's mobile computing device and/or by a vehicle. The mobile computing device or vehicle can generate and transmit breadcrumb data as a delivery resource traverses a route).
Regarding claim 11, Mouli in view of Turner discloses causing an indication of the determined one or more adjustments to the production process via a graphical user interface, GUI to be provided on a display of an electronic device (Mouli ¶44 - FIG. 2 depicts an exemplary extract of route information for a particular route and delivery resource... Mouli ¶32 - The route information in the route database 120 is also updated routinely or periodically as the processes in here occur and determine route updates. The updated routes can form a new baseline for route information).
Claims 2, 6, 7, 12 are rejected under 35 U.S.C. 103 as being unpatentable over Mouli et al. (US 20210182787 A1) in view of Turner et al. (US 20200334637 A1) further in view of Bomer et al. (CN 114938670 A)
Regarding claim 2, Mouli in view of Turner discloses a computer-implemented method for indicating time discrepancies in a repeatable process performed by a plurality of vehicles (Mouli ABS - Systems and methods for analyzing and improving delivery resource operation and efficiency).
Mouli in view of Turner does not clearly disclose the repeatable process is a repeatable process performed by a plurality of vehicles which comprises at least one of: a repeatable production process; a repeatable construction site process; a repeatable mining site process; a repeatable factory process; a repeatable harbour process; a repeatable transport depot process; and a repeatable airport process.
Bomer, from the same field of endeavor, teaches the repeatable process is a repeatable process performed by a plurality of vehicles which comprises at least one of: a repeatable production process; a repeatable construction site process; a repeatable mining site process; a repeatable factory process; a repeatable harbour process; a repeatable transport depot process; and a repeatable airport process (Bomer - The invention relates to a system and method for tracking activity of machine on the construction site).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the route analysis methodology/system of Mouli by including the machine tracking techniques of Bomer because Bomer discloses “site managers are difficult to track what machines and assets are present at any particular time site, where such machines and assets are located at the site, whether such machines and assets are in use, and/or what activities such machines and assets can engage in…The example systems and methods described herein are intended to overcome one or more of the above defects (Bomer)”. Additionally, Mouli further details that “It can also be advantageous to have accurate location and activity information for a delivery resource traversing a route and delivering to delivery points along the route. (Mouli ¶21)” so it would be obvious to consider including the additional machine tracking techniques that Bomer discloses because it would provide additional vehicle monitoring data to augment the position data collected within Mouli.
Regarding claim 6, Mouli in view of Turner discloses a computer-implemented method for indicating time discrepancies in a repeatable process performed by a plurality of vehicles (Mouli ABS - Systems and methods for analyzing and improving delivery resource operation and efficiency).
Mouli in view of Turner lacks the repeatable process is a repeatable production process performed in a large-scale construction area.
Bomer, from the same field of endeavor, teaches the repeatable process is a repeatable production process performed in a large-scale construction area (Bomer - The invention relates to a system and method for tracking activity of machine on the construction site).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the route analysis methodology/system of Mouli by including the machine tracking techniques of Bomer because Bomer discloses “site managers are difficult to track what machines and assets are present at any particular time site, where such machines and assets are located at the site, whether such machines and assets are in use, and/or what activities such machines and assets can engage in…The example systems and methods described herein are intended to overcome one or more of the above defects (Bomer)”. Additionally, Mouli further details that “It can also be advantageous to have accurate location and activity information for a delivery resource traversing a route and delivering to delivery points along the route. (Mouli ¶21)” so it would be obvious to consider including the additional machine tracking techniques that Bomer discloses because it would provide additional vehicle monitoring data to augment the position data collected within Mouli.
Regarding claim 7, Mouli in view of Turner further in view of Bomer discloses based on the identified one or more time discrepancies in the repeatable production process, determining one or more adjustments to the repeatable production process from a predetermined set of recommended adjustments (Mouli ¶71 - At block 630, the baseline information is updated {i.e. adjusted}...In another example, the server 110 may adjust the delivery route itself, for example, by changing a driving direction based on a detected recurring deviation, shifting one or more delivery points to a different route or adding delivery points from a different route based on the delivery route repeatedly taking more or less time than expected).
Bomer further teaches the repeatable process is a repeatable production process performed in a large-scale construction area (Bomer - The invention relates to a system and method for tracking activity of machine on the construction site).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the route analysis methodology/system of Mouli by including the machine tracking techniques of Bomer because Bomer discloses “site managers are difficult to track what machines and assets are present at any particular time site, where such machines and assets are located at the site, whether such machines and assets are in use, and/or what activities such machines and assets can engage in…The example systems and methods described herein are intended to overcome one or more of the above defects (Bomer)”. Additionally, Mouli further details that “It can also be advantageous to have accurate location and activity information for a delivery resource traversing a route and delivering to delivery points along the route. (Mouli ¶21)” so it would be obvious to consider including the additional machine tracking techniques that Bomer discloses because it would provide additional vehicle monitoring data to augment the position data collected within Mouli.
Regarding claim 12, Mouli in view of Turner discloses a computer-implemented method for indicating time discrepancies in a repeatable process performed by a plurality of vehicles (Mouli ABS - Systems and methods for analyzing and improving delivery resource operation and efficiency), the method comprising: receiving location data comprising a plurality of data points, each data point corresponding to a time-marked geographical position reported by a vehicle (Mouli ¶67 - At block 610, period location data is received. For example, the server 110 may receive the periodic location data transmitted by a mobile computing device 140. The periodic location data includes a number of location data points generated at the mobile computing device 140 on a periodic basis. For example, the mobile computing device 140 may generate a location data point based on GPS or other location technology, every 0.1 second, every 0.5 second, every second, every five seconds, every ten seconds, or longer, or at any other suitable interval. In some embodiments, the periodic location data is breadcrumb data collected at a constant interval to provide a record of a delivery resource's movement while servicing a delivery route); processing the location data to determine at least one data point subset which is indicative of a candidate point-of-interest, POI for the repeatable process (Mouli ¶69 - At block 620, the periodic location data is segmented into events and route segments for the delivery route based at least in part on the route information. For example, breadcrumb data collected at a mobile computing device 140 can be segmented into route segments and events as shown in FIG. 2, based on geofence data associated with the events and route segments); comparing the determined data point subset with one or more data point subsets associated with predetermined POls of a repeatable process (Mouli ¶56 -the server 110 can analyze the breadcrumb data for the out of bounds travel 276 to determine whether the out of bounds travel 276 occurs regularly for the carrier traversing the route, whether they are anomalies etc., by comparing a current days route information and delivery information with average or baseline route data, and/or with route and delivery data for a preset period, e.g., previous two weeks, a random selection of days, and the like); based on an output of the comparison: identifying a number of POls of the repeatable process (Mouli ¶46 - The delivery point section 264 includes columns identifying the types of delivery points along a route); comparing time information associated with one of the determined POls of the repeatable process with time information associated with the one or more predetermined Pols (Mouli ¶70 - At block 625, the segmented periodic location data is compared to the baseline information. In one example, the server 110 may compare the durations of the individual events and route segments of the segmented periodic location data to the stored baseline durations corresponding to the same events and route segments), and based on an output of the comparison of time information, identifying one or more time discrepancies in the repeatable process (Mouli ¶61 - The server 110 can compare the average times to the actual times for a given day and can identify any anomalies or excessive differences between the two).
Mouli in view of Turner lacks using an output of the comparison of one or more data point subsets of the location data with one or more subsets of data points associated with predetermined points-of- interest to train the machine-learning model to detect points-of-interest, or using the output of a comparison of time information associated with one of the determined points-of-interest with time information associated with one or more of the predetermined point-of-interests to train the machine learning model to detect time- discrepancies.
Bomer, from the same field of endeavor, teaches using an output of the comparison of one or more data point subsets of the location data with one or more subsets of data points associated with predetermined points-of- interest to train the machine-learning model to detect points-of-interest, or using the output of a comparison of time information associated with one of the determined points-of-interest with time information associated with one or more of the predetermined point-of-interests to train the machine learning model to detect time- discrepancies (Bomer - receiving the activity report comprising the first position data and the activity data of the intelligent machine on the construction site by the computing system, and training the machine learning model by the computing system based on the first position data and the activity data. The method may further include receiving by the computing system comprises one or more location reports about the second location data of one or more standard machines on the site, and using a machine learning model by the computing system and based on the second location data to generate predictive activity data corresponding to one or more standard machines, The predictive activity data identifies at least one predictive activity of one or more standard machines).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the route analysis methodology/system of Mouli by including the machine tracking techniques of Bomer because Bomer discloses “site managers are difficult to track what machines and assets are present at any particular time site, where such machines and assets are located at the site, whether such machines and assets are in use, and/or what activities such machines and assets can engage in…The example systems and methods described herein are intended to overcome one or more of the above defects (Bomer)”. Additionally, Mouli further details that “It can also be advantageous to have accurate location and activity information for a delivery resource traversing a route and delivering to delivery points along the route. (Mouli ¶21)” so it would be obvious to consider including the additional machine tracking techniques that Bomer discloses because it would provide additional vehicle monitoring data to augment the position data collected within Mouli.
Response to Arguments
Applicant's arguments filed 8/28/2025 have been fully considered but they are not persuasive and/or are moot in light of the new rejections addressed above.
As addressed above and in light of Applicant’s amendments, the 35 USC § 112(b) rejections are withdrawn.
Regarding the arguments related to the 35 USC § 101 rejections, as addressed above according to the guidance for 35 USC § 101 rejections contained within MPEP 2106, the Examiner maintains that the claimed invention is an abstract idea, without significantly more, and not integrated into a practical application.
The Applicant argues that the claimed invention is patent eligible because it is an improvement to a technological device (computer). Examiner does not find this persuasive because the claimed invention is not interpreted as an improvement to a computer (e.g. increased processor speeds, increased memory density), but rather an improvement to an information gathering and analysis technique that happens to take place by means of a computer.
Regarding the 35 USC § 102 and 35 USC § 103 rejections on the original Office action, Applicant amended the independent claims to further limit the claims with respect to filtering higher frequency stops. In light of this amendment, Examiner agrees that the original references did not teach this, however the amendment necessitated further search and consideration. As a result of this further search and consideration, prior art was found that does teach these limitations (Turner as discussed above). As such, Applicant’s arguments (with respect to the independent claims and their respective dependent claims) are unpersuasive.
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 Michael R Koester whose telephone number is (313)446-4837. The examiner can normally be reached Monday thru Friday 8:00AM-5:00 PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O'Connor can be reached at (571) 272-6787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL R KOESTER/Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624
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