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 .
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.
Claim Status
This action is in response to applicant’s response filed on 10/24/2025. Claims 21-39 are pending and considered below.
Response to Arguments
Claims 21-24, 26, 28-31, 35-36 and 38-39 were rejected under 35 U.S.C. 102(a)(2) as being anticipated by Levihn et al. (U.S. Patent Number 11,555,706). Applicant argued that none of the feature vectors described in Levihn pertain to characteristics of a vehicle’s occupants or objects located within the vehicle’s environment. Examiner disagrees.
Applicant’s specification discloses that feature vectors include a position or a speed of an object (paragraph [0040] (PGPub)), a structure or shape of a vehicle (paragraph [0125]), a traffic light violation (paragraph [0127]), a likelihood of a collision (paragraphs [0128] and [0183]), a level of passenger comfort (paragraphs [0129] and [0184]), a lateral clearance of an AV to a vehicle (paragraph [0130]), a position and location of a vehicle (paragraph [0131]), a size, shape, speed and direction of a vehicle (paragraph [0148]), a position and speed of a vehicle (paragraph [0151]), a spatiotemporal location of a vehicle (paragraph [0159]), and a lateral clearance of a vehicle to an object (paragraph [0181]). Examiner notes that “a vehicle” refers to a second vehicle in the operating environment of an autonomous vehicle (see, paragraph [0112] and FIG. 13).
Levihn discloses sensing the velocity, position, or heading with respect to other vehicles (col. 10, lines 23-31). This corresponds to the first feature vectors provided in applicant’s disclosure, paragraphs [0040], [0131], [0151] and [0159]. Levihn discloses occupant-oriented sensors (which may, for example, include cameras pointed primarily towards occupants’ faces, or physiological signal detectors such as heart rate detectors and the like, and may be able to provide evidence of the comfort level or stress level of the occupants) (col. 8, lines 20-24). This corresponds to the second feature vectors provided in applicant’s disclosure, paragraphs [0129] and [0184].
Claims 21-24, 26, 28-31, 35-36 and 38-39 remain rejected under 35 U.S.C. 102(a)(2) as being anticipated by Levihn for the reasons given below.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 21-39 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent Number 11,899,464. Although the claims at issue are not identical, they are not patentably distinct from each other because:
“storing, using one or more processors of an autonomous vehicle located within an environment, a plurality of constraints for operating the autonomous vehicle within the environment” of U.S. Patent Number 11,899,464 is the same as “storing, using one or more processors of an autonomous vehicle located within an environment, a plurality of constraints for operating the autonomous vehicle within the environment” of the instant application;
“receiving, using one or more sensors of the autonomous vehicle, sensor data describing the environment” of U.S. Patent Number 11,899,464 is the same as “receiving, using one or more sensors of the autonomous vehicle, sensor data describing the environment” of the instant application;
“receiving, using one or more additional sensors of the autonomous vehicle, additional sensor data describing one or more physical characteristics of a passenger of the autonomous vehicle other than a driver of the autonomous vehicle” of U.S. Patent Number 11,899,464 is the same as “receiving, using one or more additional sensors of the autonomous vehicle, additional sensor data describing one or more physical characteristics of a passenger of the autonomous vehicle other than a driver of the autonomous vehicle” of the instant application;
“extracting, using the one or more processors, a feature vector from the stored plurality of constraints, the received sensor data, and the received additional sensor data, wherein the feature vector comprises: a first feature describing an object located within the environment, a second feature describing the one or more physical characteristics of the passenger of the autonomous vehicle” of U.S. Patent Number 11,899,464 is the same as “extracting, using the one or more processors, a feature vector from the stored plurality of constraints, the received sensor data, and the received additional sensor data, wherein the feature vector comprises: a first feature describing an object located within the environment, a second feature describing the one or more physical characteristics of the passenger of the autonomous vehicle” of the instant application;
“generating, using the one or more processors, a first motion segment based on (i) the feature vector including the first feature, the second feature” of U.S. Patent Number 11,899,464 is equivalent to “generating, using a machine learning circuit of the autonomous vehicle, a first motion segment based on the feature vector including the first feature and the second feature” of the instant application;
“such that a number of violations of the stored plurality of constraints is below a threshold” of U.S. Patent Number 11,899,464 is the same as “such that a number of violations of the stored plurality of constraints is below a threshold” of the instant application;
“wherein the generated first motion segment comprises at least one of: a trajectory between two spatiotemporal locations of the environment, or a speed of the autonomous vehicle to avoid a collision of the autonomous vehicle with the object” of U.S. Patent Number 11,899,464 is the same as “wherein the generated first motion segment comprises at least one of: a trajectory between two spatiotemporal locations of the environment, or a speed of the autonomous vehicle to avoid a collision of the autonomous vehicle with the object” of the instant application; and
“causing, using the one or more processors, the autonomous vehicle to autonomously traverse to a destination in accordance with the generated first motion segment” of U.S. Patent Number 11,899,464 is the same as “causing, using the one or more processors, the autonomous vehicle to autonomously traverse to a destination in accordance with the generated first motion segment” of the instant application.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 21-24, 26, 28-31, 35-36 and 38-39 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Levihn et al. (U.S. Patent Number 11,555,706, hereinafter Levihn).
Regarding claim 21, Levihn discloses:
storing, using one or more processors of an autonomous vehicle located within an environment (col. 21, line 1 - col. 22, line 61; and FIG. 12, computing device-9000, processor(s) - 9010, main memory-9020, executable instructions-9025, and data-9026);
a plurality of constraints for operating the autonomous vehicle within the environment (col. 5, lines 32-63; col. 7, lines 28-32; col. 19, lines 17-53; FIG. 1, vehicle-110, local sensor collection-112, perception subsystem-113, communication devices-114, motion-related decision components-116, behavior planner-117, motion selector-118, motion control subsystems-120, realized trajectories-122, state prediction subsystem-133, motion control directives-134, tactical map analysis models-135, dynamic entity models-137, and data center-based machine learning resources-175; and FIG. 11, at computing devices installed, obtain a tactical map comprising information about static components of the operating environment-1101, generate a graph representation, in which the static components are represented by nodes, and edges with respective edge types indicate different semantic relationships among pairs of static components, the graph may be considered homogenized in that each node is represented as having the same number and type of edges-1104, and provide the graph representation as input to a neural network model trained to reason on graph data structures-1107);
receiving, using one or more sensors of the autonomous vehicle, sensor data describing the environment (col. 8, lines 3-33);
receiving, using one or more additional sensors of the autonomous vehicle, additional sensor data describing one or more physical characteristics of a passenger of the autonomous vehicle other than a driver of the autonomous vehicle (col. 8, lines 20-24);
extracting, using the one or more processors, a feature vector from the stored plurality of constraints, the received sensor data, and the received additional sensor data, wherein the feature vector comprises: a first feature describing an object located within the environment (col. 6, lines 7-18; col. 10, lines 23-51; and FIG. 2, other vehicles-201, origin-210, destination-215, objects-241, vehicle-250, and problem characteristics-261);
a second feature describing the one or more physical characteristics of the passenger of the autonomous vehicle (col. 8, lines 20-24);
generating, using a machine learning circuit of the autonomous vehicle, a first motion segment based on the feature vector including the first feature and the second feature (col. 11, line 57 - col. 12, line 47; and FIG. 3, raw tactical map information-302, static components of operation environment with associated properties-304, relationships among static components-306, homogenized graph representation-310, nodes-312, edges-314, DNN-based model(s) - 350, other machine learning model(s) for dynamic entities-360, combining algorithms/models-365, and state predictions, recommended actions/policies-370);
such that a number of violations of the stored plurality of constraints is below a threshold (col. 5, line 64 - col. 6, line 18);
wherein the generated first motion segment comprises at least one of: a trajectory between two spatiotemporal locations of the environment, or a speed of the autonomous vehicle to avoid a collision of the autonomous vehicle with the object (col. 19, lines 53-58; and FIG. 11, use results generated by neural network model, e.g., in combination with results of analyzing/predicting dynamic entity behavior, to generate motion-control directives-1110); and
causing, using the one or more processors, the autonomous vehicle to autonomously traverse to a destination in accordance with the generated first motion segment (col. 19, lines 58-62; and FIG. 11, transmit directives to cause movement along a particular path or trajectory-1113).
Regarding claim 22, Levihn further discloses:
wherein the generated first motion segment further comprises a directional orientation of the autonomous vehicle to avoid a collision with the object (col. 19, lines 53-62).
Regarding claim 23, Levihn further discloses:
wherein a second feature of the extracted feature vector comprises at least one of: a spatiotemporal location of the object, a speed of the object, or a directional orientation of the object (col. 10, lines 23-51).
Regarding claim 24, Levihn further discloses:
wherein: a third feature of the extracted feature vector comprises at least one of a maximum speed of the autonomous vehicle, a maximum acceleration of the autonomous vehicle, or a maximum jerk of the autonomous vehicle (col. 5, lines 57-60); and
the at least one of the maximum speed, the maximum acceleration, or the maximum jerk correspond to a level of passenger comfort measured by one or more passenger sensors of the autonomous vehicle (col. 8, lines 20-24).
Regarding claim 26, Levihn further discloses:
aggregating, using the one or more processors, a plurality of features of the extracted feature vector into a motion planning graph, wherein: the motion planning graph comprises a plurality of edges, and each edge of the plurality of edges corresponds to a motion segment of the received plurality of motion segments (col. 5, lines 32-63).
Regarding claim 28, Levihn further discloses:
wherein: the motion planning graph comprises a minimum-violation motion planning graph (col. 5, lines 32-63; and col. 11, lines 28-56); and
each edge of the plurality of edges is associated with a value of an operational metric of a corresponding motion segment (col. 5, lines 32-63; and col. 11, lines 28-56).
Regarding claim 29, Levihn further discloses:
further comprising generating, using the machine learning circuit, the value of the operational metric of each corresponding motion segment of the plurality of edges of the motion planning graph based on the feature vector (col. 5, lines 32-63; and col. 11, line 28 - col. 12, line 47).
Regarding claim 30, Levihn further discloses:
wherein the generating of the first motion segment comprises identifying, using the machine learning circuit, for each edge of the plurality of edges of the motion planning graph, a likelihood that the causing of the autonomous vehicle to autonomously traverse to the destination in accordance with a corresponding motion segment causes the operational metric to be below the threshold (col. 5, line 32 - col. 6, line 18; and col. 11, line 28 - col. 12, line 47).
Regarding claim 31, Levihn further discloses:
further comprising sampling, using the one or more processors, the stored plurality of constraints and the received sensor data to generate a third motion segment for operating the autonomous vehicle within the environment, wherein the operating of the autonomous vehicle in accordance with the third motion segment causes the operational metric associated with operating the autonomous vehicle to be below the threshold (col. 5, line 32 - col. 6, line 18; and col. 11, line 28 - col. 12, line 47).
Regarding claim 35, Levihn further discloses:
wherein the additional sensor data comprises at least one of: a heart rate of the passenger, a temperature of the passenger, or a pupil dilation of the passenger (col. 8, lines 20-24).
Regarding claim 36, Levihn further discloses:
wherein the additional sensor data comprises at least one of: a facial expressions of the passenger, or a skin conductance of the passenger (col. 8, lines 20-24).
Regarding claim 38, Levihn further discloses:
one or more computer processors, and one or more non-transitory storage media storing instructions which, when executed by the one or more computer processors, cause the one or more computer processors to: (col. 21, line 1 - col. 22, line 61; and FIG. 12, computing device-9000, processor(s) - 9010, main memory-9020, executable instructions-9025, and data-9026);
store a plurality of constraints for operating the autonomous vehicle within an environment (col. 5, lines 32-63; col. 7, lines 28-32; col. 19, lines 17-53; FIG. 1, vehicle-110, local sensor collection-112, perception subsystem-113, communication devices-114, motion-related decision components-116, behavior planner-117, motion selector-118, motion control subsystems-120, realized trajectories-122, state prediction subsystem-133, motion control directives-134, tactical map analysis models-135, dynamic entity models-137, and data center-based machine learning resources-175; and FIG. 11, at computing devices installed, obtain a tactical map comprising information about static components of the operating environment-1101, generate a graph representation, in which the static components are represented by nodes, and edges with respective edge types indicate different semantic relationships among pairs of static components, the graph may be considered homogenized in that each node is represented as having the same number and type of edges-1104, and provide the graph representation as input to a neural network model trained to reason on graph data structures-1107);
receive, using one or more sensors of the autonomous vehicle, sensor data describing the environment (col. 8, lines 3-33);
receive, using one or more additional sensors of the autonomous vehicle, additional sensor data describing one or more physical characteristics of a passenger of the autonomous vehicle other than a driver of the autonomous vehicle (col. 8, lines 20-24);
extract a feature vector from the stored plurality of constraints, the received sensor data, and the received additional sensor data, wherein the feature vector comprises: a first feature describing an object located within the environment (col. 6, lines 7-18; col. 10, lines 23-51; and FIG. 2, other vehicles-201, origin-210, destination-215, objects-241, vehicle-250, and problem characteristics-261);
a second feature describing the one or more physical characteristics of the passenger of the autonomous vehicle (col. 8, lines 20-24);
generate, using a machine learning circuit of the autonomous vehicle, a first motion segment based on the feature vector including the first feature and the second feature (col. 11, line 57 - col. 12, line 47; and FIG. 3, raw tactical map information-302, static components of operation environment with associated properties-304, relationships among static components-306, homogenized graph representation-310, nodes-312, edges-314, DNN-based model(s) - 350, other machine learning model(s) for dynamic entities-360, combining algorithms/models-365, and state predictions, recommended actions/policies-370);
such that a number of violations of the stored plurality of constraints is below a threshold (col. 5, line 64 - col. 6, line 18);
wherein the generated first motion segment comprises at least one of: a trajectory between two spatiotemporal locations of the environment, or a speed of the autonomous vehicle to avoid a collision of the autonomous vehicle with the object (col. 19, lines 53-58; and FIG. 11, use results generated by neural network model, e.g., in combination with results of analyzing/predicting dynamic entity behavior, to generate motion-control directives-1110); and
causing the autonomous vehicle to autonomously traverse to a destination in accordance with the generated first motion segment (col. 19, lines 58-62; and FIG. 11, transmit directives to cause movement along a particular path or trajectory-1113).
Regarding claim 39, Levihn further discloses:
One or more non-transitory storage media storing instructions which, when executed by one or more computing devices, cause the one or more computing devices to: (col. 21, line 1 - col. 22, line 61; and FIG. 12, computing device-9000, processor(s) - 9010, main memory-9020, executable instructions-9025, and data-9026);
store a plurality of constraints for operating an autonomous vehicle within an environment (col. 5, lines 32-63; col. 7, lines 28-32; col. 19, lines 17-53; FIG. 1, vehicle-110, local sensor collection-112, perception subsystem-113, communication devices-114, motion-related decision components-116, behavior planner-117, motion selector-118, motion control subsystems-120, realized trajectories-122, state prediction subsystem-133, motion control directives-134, tactical map analysis models-135, dynamic entity models-137, and data center-based machine learning resources-175; and FIG. 11, at computing devices installed, obtain a tactical map comprising information about static components of the operating environment-1101, generate a graph representation, in which the static components are represented by nodes, and edges with respective edge types indicate different semantic relationships among pairs of static components, the graph may be considered homogenized in that each node is represented as having the same number and type of edges-1104, and provide the graph representation as input to a neural network model trained to reason on graph data structures-1107);
receive, using one or more sensors of the autonomous vehicle, sensor data describing the environment (col. 8, lines 3-33);
receive, using one or more additional sensors of the autonomous vehicle, additional sensor data describing one or more physical characteristics of a passenger of the autonomous vehicle other than a driver of the autonomous vehicle (col. 8, lines 20-24);
extract a feature vector from the stored plurality of constraints, the received sensor data, and the received additional sensor data, wherein the feature vector comprises: a first feature describing an object located within the environment (col. 6, lines 7-18; col. 10, lines 23-51; and FIG. 2, other vehicles-201, origin-210, destination-215, objects-241, vehicle-250, and problem characteristics-261);
a second feature describing the one or more physical characteristics of the passenger of the autonomous vehicle (col. 8, lines 20-24);
generate, using a machine learning circuit of the autonomous vehicle, a first motion segment based on the feature vector including the first feature and the second feature (col. 11, line 57 - col. 12, line 47; and FIG. 3, raw tactical map information-302, static components of operation environment with associated properties-304, relationships among static components-306, homogenized graph representation-310, nodes-312, edges-314, DNN-based model(s) - 350, other machine learning model(s) for dynamic entities-360, combining algorithms/models-365, and state predictions, recommended actions/policies-370);
such that a number of violations of the stored plurality of constraints is below a threshold (col. 5, line 64 - col. 6, line 18);
wherein the generated first motion segment comprises at least one of: a trajectory between two spatiotemporal locations of the environment, or a speed of the autonomous vehicle to avoid a collision of the autonomous vehicle with the object (col. 19, lines 53-58; and FIG. 11, use results generated by neural network model, e.g., in combination with results of analyzing/predicting dynamic entity behavior, to generate motion-control directives-1110); and
causing the autonomous vehicle to autonomously traverse to a destination in accordance with the generated first motion segment (col. 19, lines 58-62; and FIG. 11, transmit directives to cause movement along a particular path or trajectory-1113).
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.
Claim 25 is rejected under 35 U.S.C. 103 as being unpatentable over Levihn, as applied to claim 21 above, and further in view of Phillips et al. (US-2019/0377351-A1, hereinafter Phillips).
Regarding claim 25, Levihn does not disclose determining whether causing an autonomous vehicle to transverse to a destination causes a traffic light violation. However, Phillips discloses a gridlock solver for a motion planning system of an autonomous vehicle, including the following features:
wherein a fourth feature of the extracted feature vector represents whether the causing of the autonomous vehicle to autonomously transverse to the destination in accordance with the first motion segment causes a traffic light violation (paragraphs [0063] and [0132]; and FIG. 4, object classifier-400, and yield zone generator-406).
Phillips teaches that yield zones are established at traffic lights, stop signs, and crosswalks. These yield zones are defined as a spatial and temporal region that an autonomous vehicle cannot be within (paragraph [0132]). It would have been obvious for a person of ordinary skill in the art at the time of the effective filing date of the claimed invention to incorporate the autonomous vehicle traffic light yield zones of Phillips into the autonomous vehicle tactical maps of Levihn. A person of ordinary skill would have been motivated to do so, with a reasonable expectation of success, for the purpose of preventing an autonomous vehicle from driving through a red light or blocking an intersection. A person of ordinary skill would be familiar with the need to plan ahead so that a vehicle does not run a red light or become trapped in an intersection.
Claim 37 is rejected under 35 U.S.C. 103 as being unpatentable over Levihn, as applied to claim 21 above, and further in view of Kishi et al. (US-2017/0313319-A1, hereinafter Kishi).
Regarding claim 37, Levihn does not disclose sensing pressure applied to a seat arm rest of a vehicle by a passenger. However, Kishi discloses a semi-autonomous vehicle with a physical state recognition unit which recognizes a driver’s pressing pressure on an arm rest as an indication of the physical state of the driver, including the following features:
wherein the additional sensor data comprises a pressure applied to a seat arm rest of the vehicle by the passenger (paragraph [0131]; and FIG. 8, ECU-30, and physical state recognition unit-35).
Kishi teaches that a semi-autonomous vehicle should transition between manual and autonomous driving based on driving readiness of a driver and a driving task demand (paragraph [0163]). Kishi further teaches that the driver’s pressing pressure on an arm rest is an indication of the physical state of the driver (paragraph [0131]). It would have been obvious for a person of ordinary skill in the art at the time of the effective filing date of the claimed invention to incorporate the arm rest pressure sensor of Kishi into the autonomous vehicle tactical maps of Levihn. A person of ordinary skill would have been motivated to do so, with a reasonable expectation of success, for the purpose of detecting the stress level of a vehicle occupant. A person of ordinary skill would understand the need to reduce stress for a vehicle occupant.
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 TAMARA L WEBER whose telephone number is (303)297-4249. The examiner can normally be reached 8:30-5:00 MTN.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Faris Almatrahi can be reached at 3134464821. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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TAMARA L. WEBER
Examiner
Art Unit 3667
/TAMARA L WEBER/Examiner, Art Unit 3667
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