DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This is in response to Applicant’s case, no. 18/934,197, with an effective filing date of 10/31/2024 . Claims 1-20 are currently pending.
Response to Arguments
Examiner acknowledges that the necessary changes were made regarding the Drawing, Specification, and Claim Objection sections in Applicant’s arguments, see pp. 17-18, and subsequently withdraws objections to said sections. However, based on the amendments made to the claims, a new set of objections are hereby made as detailed further below.
Examiner acknowledges the changes made regarding 35 USC § 101 to claims 16-20 found in Applicant’s arguments, see pg. 19, regarding the non-statutory subject matter. The Examiner has considered the amended claim limitation non-transitory computer-readable storage media and the amendment properly integrates the judicial exception into a practical application.
Therefore, the rejection based on35 USC § 101 is hereby withdrawn.
Examiner acknowledges that the necessary changes were made regarding the rejection of claim(s) 1-20 under 35 USC § 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter regarded as the invention due to containing relative terminology in Applicant’s arguments, see pg.18, and subsequently withdraws the 35 USC § 112(b) rejection to said claims.
Regarding the 35 USC § 102(a)(1) rejection of claims 1-20 as being anticipated by Ehsanibenafati et al. (US Pat. Pub. No. 2022/0144304 A1) [hereinafter referred to as Ehsanibenafati], the Applicant has elected to amend the aforementioned claims. Therefore, the Examiner’s rejection in the previous Office Action based on 35 USC § 102 is rendered moot. However, due to said amendments, new reference Weston et al. (US Pat. Pub. No. 2022/0306119 A1) [hereinafter referred to as Weston] has been necessitated. Therefore, a new rejection based on 35 USC § 103 has been made and is discussed in detail below.
Regarding claim 1, Applicant argues that Ehsanibenafati does not disclose the limitation determining a level of complexity, associated with the signal, from different levels of complexity of events occurring during an operation of the machine, wherein the different levels of complexity include: a first level of complexity associated with a single threshold, a second level of complexity associated with a plurality of thresholds, and a third level of complexity associated with a combination of weighted signals; ... wherein the level of complexity is determined and the event is detected prior to data fusion being performed on signals received from a plurality of sensors of the machine. However, Weston teaches in Table 1, below, that sensor input control parameters effect the output control parameter. The initial parameter is simply vehicle speed (e.g., first level of complexity). In the following two speed parameters, time-to-collision (TTC) and distance to follow lead vehicle both incorporate distance parameters that are used to manage the speed of the vehicle (e.g., second level of complexity). Lastly, in the detected location classification and detected environmental condition that trigger adjustments to sensor weights, it is construed that the system detects a nature of the situation and prioritizes the inputs, and therefore the outputs, based on adjusted weights given when entering the data fusion process (i.e., if the distance threshold is met as it is becoming too close to the lead vehicle, then the vehicle would slow to avoid collision even if the speed is below the threshold, hence, the distance parameter has been adjusted to have a higher weight and appears to be a third level of complexity). It is also construed that the level of complexity and the detection of the event occur prior to the performance of data fusion based on the table’s description.
Therefore, this argument is moot.
In regards to independent claims 10 and 16, Applicant argues, while differing in scope, these claims recite similar features to claim 1 and their rejections should likewise be withdrawn.
However, this argument is unpersuasive for the same reasons as given above.
Applicant argues the dependent claims are patentable by virtue of their dependency.
This argument is unpersuasive as each independent claim has been fully rejected for the reasons as given above.
Claim Objections
Claim(s) 1 and 16 is/are objected to because of the following informalities:
Claim 1 line 9 appears to contain a typographical error where processing the signal based on the level of complexity should be corrected to processing the signal based on the determined level of complexity;
Claim 16 lines3-4 contain a typographical error where one or more computer-readable should be corrected to one or more non-transitory computer-readable
Appropriate correction is required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ehsanibenafati et al. (US Pat. Pub. No. 2022/0144304 A1), hereinafter referred to as Ehsanibenafati, in view of Weston et al. (US Pat. Pub. No. 2022/0306119 A1), hereinafter referred to as Weston.
Regarding claim 1, Ehsanibenafati discloses:
A method, comprising:
receiving a signal from a sensor of a machine ([0002] sentence (s.) 3, for many automation systems, there can be various sensors and sources that provide data useful in determining such paths, which is construed as a machine receiving a signal from a sensor);
determining a level of complexity, associated with the signal, from different levels of complexity of events occurring during an operation of the machine ([0041] s. 1-2, a control system can determine one or more actions to take at any given time that correspond to that task which may include making one or more adjustments, such as to a steering or braking system, to cause the vehicle to maneuver in a determined way along a determined path, such as a path that causes the vehicle to navigate down a current lane of a road, within the lane markers, while avoiding collisions and operating at an appropriate speed, which is construed as determining levels of complexity associated with a signal during the operations of the vehicle where the specific actions would necessarily have differing levels of complexity (e.g., staying in a lane versus collision avoidance)),
wherein the different levels of complexity include:
a first level of complexity associated with a single threshold ([0041] s.2, operating at an appropriate speed, which is construed as a single threshold),
a second level of complexity associated with a plurality of thresholds ([0041] s.2, operating at an appropriate speed and [0038] distance estimates for all objects in the image including measuring a distance from a target object (e.g., a preceding vehicle) and use generated information (e.g., metadata) to activate autonomous emergency braking and lane departure warning functions, which is construed as a plurality of thresholds), and
processing the signal based on the level of complexity ([see [0041] s.1-2 as discussed above and [0157] s.6, different categories with high or low priority based on the emergency level of the path);
detecting an event based on processing the signal based on the level of complexity ([see [0041] s.1-2 as discussed above); and
providing an indication that the event has been detected to one or more components of the machine to cause the one or more components to control the operation of the machine ([0029] controller(s) provide signals for controlling one or more components and/or systems of vehicle in response to sensor data received from one or more sensors (e.g., sensor inputs).).
However, although Ehsanibenafati discloses in [0049] s.5, during localization various methods for combining or fusing sensor data including the use of weights, it does not explicitly disclose:
a third level of complexity associated with a combination of weighted signals; and
wherein the level of complexity is determined and the event is detected prior to data fusion being performed on signals received from a plurality of sensors of the machine.
However, Weston teaches in Table 1 that sensor input control parameters effect the output control parameter. The initial parameter is simply vehicle speed (e.g., first level of complexity). In the following two speed parameters, time-to-collision (TTC) and distance to follow lead vehicle both incorporate distance parameters that are used to manage the speed of the vehicle (e.g., second level of complexity). Lastly, in the detected location classification and detected environmental condition that trigger adjustments to sensor weights, it is construed that the system detects a nature of the situation and prioritizes the inputs, and therefore the outputs, based on adjusted weights given when entering the data fusion process (i.e., if the distance threshold is met as it is becoming too close to the lead vehicle, then the vehicle would slow to avoid collision even if the speed is below the threshold, hence, the distance parameter has been adjusted to have a higher weight and appears to be a third level of complexity). It is also construed that the level of complexity and the detection of the event occur prior to the performance of data fusion based on the table’s description.
Therefore it would have been obvious to one of ordinary skill in the art of adaptive cruise control and vehicle controls before the effective filing date of the current invention to modify the autonomous vehicle control system of Ehsanibenafati, by incorporating the complexity and data fusion teachings of Weston, such that the combination would provide for the predictable result of, as acknowledged by Weston in [0006] s.2, improving safety and vehicle operations through the computer adjusting operation of one or more vehicle subsystems.
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Regarding claim 2, Ehsanibenafati, as modified by Weston, discloses:
The method of claim 1, wherein determining the level of complexity associated with the signal comprises:
determining, using a processor of the sensor, whether the level of complexity is the first level of complexity (see claim 1 regarding actions corresponding to specific tasks); and
determining whether the level of complexity is the first level of complexity or the second level of complexity (see claim 1 regarding actions corresponding to specific tasks).
Although discloses in [0095] s.4, processor bus that may transmit signals between processors and components, it does not explicitly disclose the limitation providing the signal from the sensor to a signal bus of the machine.
However, Weston is further relied upon as it teaches in [0030] a CAN bus or wireless network to communicably couple the vehicle to the processor.
Therefore it would have been obvious to one of ordinary skill in the art of adaptive cruise control and vehicle controls before the effective filing date of the current invention to modify the autonomous vehicle control system of Ehsanibenafati, as already modified by the complexity and data fusion teachings Weston, by further incorporating the signal bus teachings of Weston, such that as the complexity and data fusion teachings are considered within Ehsanibenafati, the signal bus teachings are also considered.
Regarding claim 3, Ehsanibenafati, as modified by Weston, discloses:
The method of claim 2, wherein processing the signal based on the level of complexity comprises:
processing the signal using a first detection module, associated with the processor of the sensor, when the level of complexity is the first level of complexity (see claim 1 and [0028] computing devices (e.g., supercomputers) that process sensor signals);
processing the signal using a second detection module, associated with the signal bus, when the level of complexity is the second level of complexity ([0028-29] controllers and computing devices to perform the invention); and
processing the signal using a third detection module, associated with the signal bus, when the level of complexity is the third level of complexity ([0028-29] controllers and computing devices to perform the invention).
Claim 18 recites a computer program product having substantially the same features of claim 3 above, therefore claim 18 is rejected for the same reasons as claim 3.
Regarding claim 4, Ehsanibenafati, as modified by Weston, discloses:
The method of claim 3, further comprising:
generating rules identifying the different levels of complexity,
wherein the rules are used by the first detection module, the second detection module, and the third detection module (see Fig. 14 below and [0018] where training, adapting, and deploying the models is construed as generating a set of rules to perform the corresponding actions and based on the different detection modules and sensory information).
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Claim 19 recites a computer program product having substantially the same features of claim 4 above, therefore claim 19 is rejected for the same reasons as claim 4.
Regarding claim 5, Ehsanibenafati, as modified by Weston, discloses:
The method of claim 1, wherein processing the signal based on the level of complexity comprises:
determining that the signal is associated with the first level of complexity (see claim 1 regarding actions corresponding to tasks);
determining that the signal satisfies the single threshold ([0203] s.4, system sets thresholds and considers detections that exceed the threshold); and
detecting that the event, associated with the first level of complexity, has occurred based on detecting that the signal satisfies the single threshold (see claim 1 and [0203] as discussed above).
Regarding claim 6, Ehsanibenafati, as modified by Weston, discloses:
The method of claim 5, wherein processing the signal based on the level of complexity comprises:
determining that the signal is associated with the first level of complexity (see claim 1 regarding actions corresponding to tasks);
determining that the signal does not satisfy a first threshold (see claim 5 regarding use of thresholds);
determining that the signal satisfies a second threshold after determining that the signal does not satisfy the first threshold ([0239] 3-4, system monitors and controls a distance, interpreted as an example of a second threshold, as well as the speed of the vehicle, interpreted as an example of a first threshold, and attempts to maintain a safe distance to the preceding vehicle and advises vehicle to switch lanes when necessary which may necessarily correlate not exceeding a speed threshold but exceeding a safe distance threshold which triggers a lane switch as opposed to an emergency situation where both speed and distance thresholds may be triggered); and
detecting that an event, associated with the first level of complexity, has occurred based on determining that the signal does not satisfy the first threshold (see [0239] as discussed above).
Regarding claim 7, Ehsanibenafati, as modified by Weston, discloses:
The method of claim 1, wherein processing the signal based on the level of complexity comprises:
determining that the signal is associated with the second level of complexity (see claim 1 regarding actions corresponding to tasks);
determining that the signal satisfies a first threshold of the plurality of thresholds (see claim 1 regarding actions corresponding to tasks and see claim 6 above regarding multiple thresholds triggering actions);
determining that an additional signal from an additional sensor of the machine satisfies a second threshold of the plurality of thresholds (see claim 1 regarding actions corresponding to tasks and see claim 6 above regarding multiple thresholds triggering actions); and
detecting that an event, associated with the third level of complexity, has occurred based on determining that the signal satisfies the first threshold and that the additional signal satisfies the second threshold (see claim 1 regarding actions corresponding to tasks and the use of weights, claim 6 above regarding multiple thresholds triggering actions, and [0242] s.3, regarding corrective actions applied to avoid a collision which necessarily comprise of a first threshold and a second threshold being satisfied (e.g., speed and distance)).
Claim 20 recites a computer program product having substantially the same features of claim 7 above, therefore claim 20 is rejected for the same reasons as claim 7.
Regarding claim 8, Ehsanibenafati, as modified by Weston, discloses:
The method of claim 7, wherein processing the signal based on the level of complexity comprises:
storing information regarding the signal in a data structure based on determining that the signal satisfies the first threshold ([0068] s.1-4, storing of information locally at the vehicle level in a database and this information is utilized in generating paths for the vehicle);
determining a portion of entries, of the data structure, that includes values ([0076] activation storage that are functions of input/output and/or weight parameter data stored in code and/or data storage); and
detecting that the event has occurred based on the portion of entries that includes the values (see [0068] and [0076] as discussed above).
Regarding claim 9, Ehsanibenafati, as modified by Weston, discloses:
The method of claim 1, wherein processing the signal based on the level of complexity comprises:
determining that the signal is associated with the third level of complexity (see claim 1 and claim 8 above);
determining that the signal satisfies a first weighted threshold of the plurality of thresholds (see claim 1 regarding use of thresholds, [0068] where data stored is utilized in path generation and [0076] input/output and/or weight parameter data stored in code and/or data storage);
determining that an additional signal from an additional sensor of the machine satisfies a second weighted threshold of the plurality of thresholds(see claim 1 regarding actions corresponding to tasks and see claim 6 above regarding multiple thresholds triggering actions); and
detecting that an event, associated with the third level of complexity, has occurred based on determining that the signal satisfies the first weighted threshold and that the additional signal satisfies the second weighted threshold (see claim 1 regarding actions corresponding to tasks and see claim 6 above regarding multiple thresholds triggering actions).
Claim 15 recites a system having substantially the same features of claim 9 above, therefore claim 15 is rejected for the same reasons as claim 9.
Regarding claim 10, Ehsanibenafati discloses:
A system comprising:
an event analyzer, comprising one or more processors, to generate information regarding different levels of complexity of different events detected during an operation of a vehicle (see claim 1 regarding levels of complexity and claim 2 regarding use of processors and signal buses),
wherein the different levels of complexity include:
a first level of complexity associated with a single threshold (see claim 1),
a second level of complexity associated with a plurality of thresholds (see claim 1), and
a sensor to:
generate a signal (see claim 1),
determine whether a level of complexity of events, associated with the signal, is the first level of complexity (see claim 1 regarding levels of complexity claim 6 regarding thresholds, and claim 2 regarding use of processors and signal buses);
process the signal based on the first level of complexity to determine whether a first event associated with the first level of complexity is detected (see claim 1 regarding levels of complexity claim 6 regarding thresholds, and claim 2 regarding use of processors and signal buses); and
determine whether the level of complexity is the second level of complexity or the third level of complexity(see claim 1 regarding levels of complexity claim 6 regarding thresholds, and claim 2 regarding use of processors and signal buses), and
provide the signal to be processed based on:
the second level of complexity to determine whether a second event associated with the second level of complexity is detected (see claim 1 regarding levels of complexity claim 6 regarding thresholds, and claim 2 regarding use of processors and signal buses), or
the third level of complexity to determine whether a third event associated with the third level of complexity is detected (see claim 1 regarding levels of complexity claim 6 regarding thresholds, claim 9 regarding utilizing weighted values, and claim 2 regarding use of processors and signal buses).
However, although Ehsanibenafati discloses in [0049] s.5, during localization various methods for combining or fusing sensor data including the use of weights, it does not explicitly disclose:
a third level of complexity associated with a combination of weighted signals;
a signal bus; and
detected, wherein the first event, the second event, and the third event are detected prior to data fusion being performed on signals received from a plurality of sensors of the vehicle.
However, Weston teaches in Table 1 that sensor input control parameters effect the output control parameter. The initial parameter is simply vehicle speed (e.g., first level of complexity). In the following two speed parameters, time-to-collision (TTC) and distance to follow lead vehicle both incorporate distance parameters that are used to manage the speed of the vehicle (e.g., second level of complexity). Lastly, in the detected location classification and detected environmental condition that trigger adjustments to sensor weights, it is construed that the system detects a nature of the situation and prioritizes the inputs, and therefore the outputs, based on adjusted weights given when entering the data fusion process (i.e., if the distance threshold is met as it is becoming too close to the lead vehicle, then the vehicle would slow to avoid collision even if the speed is below the threshold, hence, the distance parameter has been adjusted to have a higher weight and appears to be a third level of complexity). It is also construed that the level of complexity and the detection of the event occur prior to the performance of data fusion based on the table’s description. Weston is further relied upon as it teaches in [0030] a CAN bus or wireless network to communicably couple the vehicle to the processor.
Therefore it would have been obvious to one of ordinary skill in the art of adaptive cruise control and vehicle controls before the effective filing date of the current invention to modify the autonomous vehicle control system of Ehsanibenafati, by incorporating the signal bus, complexity, and data fusion teachings of Weston, such that the combination would provide for the predictable result of, as acknowledged by Weston in [0006] s.2, improving safety and vehicle operations through the computer adjusting operation of one or more vehicle subsystems.
Regarding claim 11, Ehsanibenafati, as modified by Weston, discloses:
The system of claim 10, wherein the sensor includes a first detection module to process the signal based on the first level of complexity to determine whether the first event is detected (see claims 2 and 3 regarding processors and different signal detection modules and claims 6 and 9 regarding thresholds and weights), and
wherein the system further comprises:
a second detection module to process the signal based on the second level of complexity to determine whether the second event is detected (see claims 2 and 3 regarding processors and different signal detection modules and claims 6 and 9 regarding thresholds and weights); and
a third detection module to process the signal based on the third level of complexity to determine whether the third event is detected (see claims 2 and 3 regarding processors and different signal detection modules and claims 6 and 9 regarding thresholds and weights).
Regarding claim 12, Ehsanibenafati, as modified by Weston, discloses:
The system of claim 11, wherein the first detection module is to provide an indication, that the first event has been detected, to one or more components of the vehicle to cause the one or more components to control the operation of the vehicle (see claims 1 and 6), and
wherein the event analyzer, the sensor, the signal bus, the second detection module, and the third detection module are included in the vehicle ([0062] for autonomous vehicles where decisions need to be made very quickly in order to ensure safety, many of these components and much of this processing may be done on the vehicle itself, in order to avoid latency and connectivity issues).
Regarding claim 13, Ehsanibenafati, as modified by Weston, discloses:
The system of claim 11, wherein the second detection module is to provide an indication, that the second event has been detected, to one or more components of the vehicle to cause the one or more components to control an operation of the vehicle (see claims 1 and 6), and
wherein the event analyzer, the sensor, the signal bus, the second detection module, and the third detection module are included in the vehicle (see claim 12).
Regarding claim 14, Ehsanibenafati, as modified by Weston, discloses:
The system of claim 11, wherein the third detection module is to provide an indication, that the third event has been detected, to one or more components of the vehicle to cause the one or more components to control an operation of the vehicle (see claims 1, 6, and 9), and
wherein the event analyzer, the sensor, the signal bus, the second detection module, and the third detection module are included in the vehicle (see claim 12).
Regarding claim 16, Ehsanibenafati discloses:
A computer program product comprising:
one or more non-transitory computer-readable storage media ([0264] s.3, computer program code is stored on computer-readable storage media); and
program instructions stored on the one or more computer- readable storage media to perform comprising ( [0264] s.1-3 discloses computer code and storage and [0219] s.5, computer control program that executes instructions):
receiving a signal from a sensor of a machine (see claim 1);
determining a level of complexity, of events associated with the signal, from different levels of complexity of events occurring during an operation of the machine (see claim 1),
wherein the different levels of complexity include:
a first level of complexity associated with a single threshold (see claim 1),
a second level of complexity associated with a plurality of thresholds (see claim 1), and
a third level of complexity associated with weighted signals (see claim 1);
processing the signal based on the level of complexity (see claim 1);
detecting an event based on processing the signal based on the level of complexity (see claim 1); and
providing an indication that the event has been detected to one or more components of the machine to cause the one or more components to control the operation of the machine (see claim 1).
However, although Ehsanibenafati discloses in [0049] s.5, during localization various methods for combining or fusing sensor data including the use of weights, it does not explicitly disclose:
a third level of complexity associated with a combination of weighted signals; and
wherein the level of complexity is determined and the event is detected prior to data fusion being performed on signals received from a plurality of sensors of the machine.
However, Weston teaches in Table 1 that sensor input control parameters effect the output control parameter. The initial parameter is simply vehicle speed (e.g., first level of complexity). In the following two speed parameters, time-to-collision (TTC) and distance to follow lead vehicle both incorporate distance parameters that are used to manage the speed of the vehicle (e.g., second level of complexity). Lastly, in the detected location classification and detected environmental condition that trigger adjustments to sensor weights, it is construed that the system detects a nature of the situation and prioritizes the inputs, and therefore the outputs, based on adjusted weights given when entering the data fusion process (i.e., if the distance threshold is met as it is becoming too close to the lead vehicle, then the vehicle would slow to avoid collision even if the speed is below the threshold, hence, the distance parameter has been adjusted to have a higher weight and appears to be a third level of complexity). It is also construed that the level of complexity and the detection of the event occur prior to the performance of data fusion based on the table’s description.
Therefore it would have been obvious to one of ordinary skill in the art of adaptive cruise control and vehicle controls before the effective filing date of the current invention to modify the autonomous vehicle control system of Ehsanibenafati, by incorporating the complexity and data fusion teachings of Weston, such that the combination would provide for the predictable result of, as acknowledged by Weston in [0006] s.2, improving safety and vehicle operations through the computer adjusting operation of one or more vehicle subsystems.
Regarding claim 17, Ehsanibenafati, as modified by Weston, discloses:
The computer program product of claim 16, wherein determining the level of complexity associated with the signal comprises:
determining, using a first filter of a processor of the sensor, whether the level of complexity is the first level of complexity (see claim 2 and [0234] regarding the use of Kalman filtering algorithms that may necessarily filter out noise from the dataset);
providing the signal from the sensor to a signal bus of the machine (see claim 2 and [0234] as discussed above); and
determining, using a second filter of the signal bus, whether the level of complexity is the first level of complexity or the second level of complexity(see claim 2 and [0234] as discussed above).
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see:
Nix et al. (US Pat. Pub. No. 2009/0254260 A1) is directed towards control an adaptive cruise control attributing different weights to information received from the various sensors based on weighting factors such as distance and speed thresholds.
Conclusion
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 KEITH ALLEN VON VOLKENBURG whose telephone number is (703)756-5886. The Examiner can normally be reached Monday-Friday 8:30 am-5:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Erin D. Bishop can be reached at (571) 270-3713. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Keith A von Volkenburg/Examiner, Art Unit 3665
/Erin D Bishop/Supervisory Patent Examiner, Art Unit 3665
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