DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant's arguments with respect to the rejections of claims 1-4, 6-7, 9-10, 12, and 14-15 under 35 U.S.C. §101 have been fully considered and are persuasive. The rejections are hereby withdrawn.
Applicant’s arguments with respect to the rejections of claims 1-4, 6-7, 9-10, 12, and 14-15 under 35 U.S.C. §103 have been fully considered but are not persuasive. As discussed in further detail below, the argued limitations of organizing the nodes of the knowledge graph with respect to scenes and sequences, wherein a sequence represents an ordered collection of scenes, is taught by Al Faruque.
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.
Claims 1-15 are rejected under 35 U.S.C. 103 as being unpatentable over US 11436504 B1, filed June 5th, 2019, hereinafter “Lukarski”, in view of US 20230230484 A1, filed January 16th, 2023, hereinafter “Al Faruque”.
Regarding claim 1, Lukarski teaches A computer-implemented method for generating a knowledge graph for traffic motion prediction. See at least figures 1 and 10.
comprising the following steps: receiving environment sensor data of at least one environment sensor of an ego-vehicle, wherein the environment sensor data represent an environment of the ego-vehicle and include information regarding at least one traffic participant located in the environment of the ego-vehicle. See at least col. 4 line 35 – col. 5 line 7, col. 8 line 43 – col. 9 line 22, col. 21 lines 1 – 18, figure 1 (block 112), and figure 10 (step 1001), wherein sensors of a vehicle collect sensor data regarding an environment of the ego vehicle 110. The sensor data includes data on static and dynamic objects in the environment surrounding the vehicle. See at least col. 13 lines 9 – 33, wherein dynamic objects include traffic participants such as cars, buses, trucks, motorcycles, trains, bicycles, pedestrians, etc.
receiving map data from an electronic road map, wherein the map data represent a road network in the environment of the ego-vehicle and include information regarding at least one motion track the at least one traffic participant is positioned on. See at least col. 4 line 35 – col. 5 line 7, col. 7 lines 48 – 64, col. 10 line 50 – col. 11 line 16, col. 12 line 22 – col. 13 line 8, col. 21 lines 1 – 18, figure 1 (blocks 114 and 175), figure 2, figure 3 (map(s) 374), and figure 10 (step 1001), wherein map data is received via communication devices from external sources. The map data represents static objects in the environment of the ego-vehicle such as lane segments, and includes information on static objects related to dynamic objects (traffic participants) in the environment.
extracting the information regarding the at least one traffic participant from the environment sensor data. See at least col. 8 line 43 – col. 9 line 22 and figure 1 (block 165), wherein analyzers extract object identification information from the environment sensor data.
and extracting the information regarding the at least one motion track the at least one traffic participant is positioned on from the map data. See at least col. 12 line 22 – col. 13 line 8, col. 19 line 22 – col. 20 line 67, and figure 9, wherein information on static objects associated with detected dynamic objects is obtained from map data 952.
generating a graph of the road network in the environment of the ego-vehicle including nodes and edges based on the map data and/or the environment sensor data. See at least col. 4 line 35 – col. 5 line 7, col. 21 lines 19 – 35, figure 1 (block 115), and figure 10 (step 1004), wherein a unified scene graph is created, representing the environment of the vehicle and including nodes and edges based on the received map and sensor data.
wherein the graph includes at least one node representing the at least one traffic participant, and at least one node representing the at least one motion track the at least one traffic participant is positioned on. See at least col. 15 line 28 – 67 and figure 6, wherein the graph 610 includes nodes P12 (pedestrian) and OV11 (other vehicle) representing traffic participants, and additionally includes nodes LS2 and LD8, representing the lane segments that the pedestrian and other vehicle, respectively, are located on.
organizing the nodes of the knowledge graph into classes and sub-classes, wherein the sub-classes include at least one node representing a specific type of the at least one traffic participant selected from the group consisting of a static object, a movable object, a vehicle, an animal, and a human. See at least col. 12 line 22 – col. 13 line 33 and figure 3, wherein a hierarchy of sub-categories are defined for the categories. For example, the category of other vehicles 373 is organized into sub-categories including cars, buses, trucks, motorcycles, trains, bicycles, etc.
wherein a scene includes information of the environment sensor data and the map data for one time stamp. See at least col. 5 lines 8 – 38, wherein the graph is organized into scenes, by including multiple nodes representing objects at particular time stamps. Edges are formed in the graph to indicate succession. For example, a tracked object in the scene at time T1 is represented by node N1, and the same object in the scene at time T2 is represented by node N2. The two nodes form the sequence N1 – μl – N2, wherein μl is used to represent the dynamics of the scene between T1 and T2.
predicting, by a motion prediction module, a future motion of the at least one traffic participant positioned in the environment of the ego-vehicle based on the knowledge graph. See at least col. 7 lines 10 – 47, col. 8 lines 25 – 42, col. 21 line 45 – col. 22 line 3, and figure 10 (step 1010), wherein the generated scene graph is used to plan the ego vehicle’s trajectory, by predicting movements of the detected dynamic objects near the ego vehicle.
and controlling the ego-vehicle based on the predicted future motion of the at least one traffic participant, the controlling including executing at least one of a steering function of the ego-vehicle, an acceleration of the ego-vehicle, or a deceleration of the ego-vehicle. See at least col. 7 lines 10 – 47, col. 8 lines 25 – 42, col. 21 line 45 – col. 22 line 15, and figure 10 (step 1010), wherein the generated scene graph is used to plan the ego vehicle’s trajectory, by predicting movements of the detected dynamic objects near the ego vehicle. The vehicle’s braking/steering/acceleration subsystems are then controlled by a motion control directive based on the planned trajectory.
Lukarski remains silent as to the specifics of generating a knowledge graph, wherein the organizing further includes organizing the nodes of the knowledge graph with respect to scenes and sequences of the environment sensor data and the map data; and wherein a sequence represents an ordered collection of scenes.
Al Faruque teaches generating a knowledge graph. See at least [0005], [0022] and figure 3, wherein the generated scene graph is a knowledge graph that encodes knowledge about a visual scene.
wherein the organizing further includes organizing the nodes of the knowledge graph with respect to scenes and sequences of the environment sensor data and the map data. See at least [0118] and figure 7, wherein the sensor data is organized into sequences (video clips) of successive scenes (image frame) during sensor data preprocessing. In combination with Lukarski’s teaching, discussed above, of environment sensor data and map data, this limitation is taught in its entirety.
and wherein a sequence represents an ordered collection of scenes. See at least [0116]-[0118], wherein a sequence represents an ordered collection of image frames, or scenes. Per pg. 23, lines 23-25 of Applicant’s Specification, a sequence 537 is equivalent to a video where its frames are scenes 533/535.
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to modify Lukarski with Al Faruque’s knowledge graph. It would have been obvious to modify because doing so enables models to assess the risk of driving maneuvers with better performance, as recognized by Al Faruque (see at least [0022] and [0027]-[0029]).
Regarding claim 2, Lukarski and Al Faruque in combination disclose all of the limitations of claim 1 as discussed above, and Lukarski additionally teaches wherein the at least one motion track the at least one traffic participant is located on is at least one out of the following list: road, lane, intersection, underpass, bridge, motorway, motorway access, motorway exit, roundabout, parking bay, parking lot, bicycle lane, tramway track, pedestrian crossing, and sidewalk. See at least col. 12 line 22 – col. 13 line 8 and figure 3, wherein the static objects include lane segments 302, overpasses 308, bridges 309, parking lots/areas 310, railroad crossing gates 313, crosswalks 304, curbs 206, etc. Additionally, see at least col. 10 line 32 – col. 11 line 16 and figure 2, wherein the map data contains additional information on lane segments including whether the segment is an exit ramp or an entrance ramp.
Regarding claim 3, Lukarski and Al Faruque in combination disclose all of the limitations of claim 1 as discussed above, and Lukarski additionally teaches wherein the map data of the electronic road map include further information regarding further features of the at least one motion track the at least one traffic participant is located on, and wherein the at least one further feature of the at least one motion track of the at least one traffic participant is integrated into the knowledge graph via at least one further node. See at least col. 7 lines 48 – 64, col. 12 line 22 – col. 13 line 8 and figure 3, wherein the map data contains additional features regarding the roadways including traffic lights, signs, medians, buildings along the lanes, geographical landmarks, etc. These features are integrated into the scene graph as additional static object nodes.
Regarding claim 4, Lukarski and Al Faruque in combination disclose all of the limitations of claim 1 as discussed above, and Lukarski additionally teaches wherein the environment sensor data further include further information regarding at least one further feature of the at least one traffic participant, and wherein the at least one further feature of the at least one traffic participant is integrated into the knowledge graph via at least one further node. See at least col. 12 line 22 – col. 13 line 33 and figure 3, wherein the sensor data is used by classifier 345 to obtain information regarding the type of objects detected. The dynamic objects 380 include sub-categories such as cars, buses, trucks, motorcycles, trains, bicycles, etc., and these are integrated into the scene graph as additional dynamic object nodes.
Regarding claim 6, Lukarski and Al Faruque in combination disclose all of the limitations of claim 3 as discussed above, and Lukarski additionally teaches wherein the further features of the at least one motion track are at least one of the following list including: road geometries, lane geometries, lane dividers, lane boundaries, lane connectors, intersections, stop areas, traffic signals, traffic signs, traffic regulations, road conditions, slope values, pedestrian crossings, car park areas, road segments, road blocks. See at least col. 7 lines 48 – 64, col. 12 line 22 – col. 13 line 8 and figure 3, wherein the map data contains additional features regarding the roadways including medians, traffic lights, signs, crosswalks, parking areas, buildings along the lanes, geographical landmarks, etc.
Regarding claim 7, Lukarski and Al Faruque in combination disclose all of the limitations of claim 1 as discussed above, and Lukarski additionally teaches wherein the further features of the at least one traffic participant are at least one of the following list including: i) static object, ii) moving object, iii) human, iv) animal, v) vehicle, vi) car, vii) truck, viii) tram, ix) motorcycle, x) bicycle, xi) barrier, xii) traffic cone, and xiii) a relative position to at least one further traffic participant and/or to the ego-vehicle. See at least col. 12 line 22 – col. 13 line 33 and figure 3, wherein the sensor data is used by classifier 345 to obtain information regarding the type of objects detected. The dynamic objects 380 include sub-categories such as cars, buses, trucks, motorcycles, trains, bicycles, pedestrians, animals, etc., and these are integrated into the scene graph as additional dynamic object nodes.
Regarding claim 9, Lukarski and Al Faruque in combination disclose all of the limitations of claim 1 as discussed above, and Lukarski additionally teaches wherein the information of the map data considered in generating the knowledge graph is limited to an area of a possible path of the at least one traffic participant. See at least col. 19 lines 22 – 50 and figure 9, wherein the graph generation only uses map information 952 from the map representing the expected vicinity of the ego vehicle during its journey.
Regarding claim 10, Lukarski and Al Faruque in combination disclose all of the limitations of claim 1 as discussed above, and Lukarski additionally teaches wherein the method is executed during a driving operation of the ego-vehicle. See at least col. 22 lines 4 – 15 and figure 10 (step 1013), wherein the steps 1001-1013 are performed iteratively as the vehicle is being controlled.
Regarding claim 12, Lukarski and Al Faruque in combination disclose all of the limitations of claim 1 as discussed above, and Lukarski additionally teaches wherein the motion prediction module includes a trained artificial intelligence capable of predicting the motion of the at least one traffic participant based on information of the knowledge graph. See at least See at least col. 7 lines 10 – 47, col. 8 lines 25 – 42, col. 21 line 45 – col. 22 line 3, and figure 10 (step 1010), wherein the predictions of future states of the vehicle’s environment are performed by trained reasoning algorithms/models. See at least col. 4 lines 16 – 34 and figure 1, wherein the reasoning algorithms/models used to plan the vehicle’s motion/behavior are machine learning models.
Regarding claim 14, Lukarski teaches A computing unit configured to generate a knowledge graph for traffic motion prediction. See at least figures 1 and 10. Additionally, see at least figure 11 (computing device 9000).
the computing unit configured to: receive environment sensor data of at least one environment sensor of an ego-vehicle, wherein the environment sensor data represent an environment of the ego-vehicle and include information regarding at least one traffic participant located in the environment of the ego-vehicle. See at least col. 4 line 35 – col. 5 line 7, col. 8 line 43 – col. 9 line 22, col. 21 lines 1 – 18, figure 1 (block 112), and figure 10 (step 1001), wherein sensors of a vehicle collect sensor data regarding an environment of the ego vehicle 110. The sensor data includes data on static and dynamic objects in the environment surrounding the vehicle. See at least col. 13 lines 9 – 33, wherein dynamic objects include traffic participants such as cars, buses, trucks, motorcycles, trains, bicycles, pedestrians, etc.
receive map data from an electronic road map, wherein the map data represent a road network in the environment of the ego-vehicle and include information regarding at least one motion track the at least one traffic participant is positioned on. See at least col. 4 line 35 – col. 5 line 7, col. 7 lines 48 – 64, col. 10 line 50 – col. 11 line 16, col. 12 line 22 – col. 13 line 8, col. 21 lines 1 – 18, figure 1 (blocks 114 and 175), figure 2, figure 3 (map(s) 374), and figure 10 (step 1001), wherein map data is received via communication devices from external sources. The map data represents static objects in the environment of the ego-vehicle such as lane segments, and includes information on static objects related to dynamic objects (traffic participants) in the environment.
extract the information regarding the at least one traffic participant from the environment sensor data. See at least col. 8 line 43 – col. 9 line 22 and figure 1 (block 165), wherein analyzers extract object identification information from the environment sensor data.
and extract the information regarding the at least one motion track the at least one traffic participant is positioned on from the map data. See at least col. 12 line 22 – col. 13 line 8, col. 19 line 22 – col. 20 line 67, and figure 9, wherein information on static objects associated with detected dynamic objects is obtained from map data 952.
generate a graph of the road network in the environment of the ego-vehicle including nodes and edges based on the map data and/or the environment sensor data. See at least col. 4 line 35 – col. 5 line 7, col. 21 lines 19 – 35, figure 1 (block 115), and figure 10 (step 1004), wherein a unified scene graph is created, representing the environment of the vehicle and including nodes and edges based on the received map and sensor data.
wherein the graph includes at least one node representing the at least one traffic participant, and at least one node representing the at least one motion track the at least one traffic participant is positioned on. See at least col. 15 line 28 – 67 and figure 6, wherein the graph 610 includes nodes P12 (pedestrian) and OV11 (other vehicle) representing traffic participants, and additionally includes nodes LS2 and LD8, representing the lane segments that the pedestrian and other vehicle, respectively, are located on.
organize the nodes of the knowledge graph into classes and sub-classes, wherein the sub-classes include at least one node representing a specific type of the at least one traffic participant selected from the group consisting of a static object, a movable object, a vehicle, an animal, and a human. See at least col. 12 line 22 – col. 13 line 33 and figure 3, wherein a hierarchy of sub-categories are defined for the categories. For example, the category of other vehicles 373 is organized into sub-categories including cars, buses, trucks, motorcycles, trains, bicycles, etc.
wherein a scene includes information of the environment sensor data and the map data for one time stamp. See at least col. 5 lines 8 – 38, wherein the graph is organized into scenes, by including multiple nodes representing objects at particular time stamps. Edges are formed in the graph to indicate succession. For example, a tracked object in the scene at time T1 is represented by node N1, and the same object in the scene at time T2 is represented by node N2. The two nodes form the sequence N1 – μl – N2, wherein μl is used to represent the dynamics of the scene between T1 and T2.
predict a future motion of the at least one traffic participant positioned in the environment of the ego-vehicle based on the knowledge graph. See at least col. 7 lines 10 – 47, col. 8 lines 25 – 42, col. 21 line 45 – col. 22 line 3, and figure 10 (step 1010), wherein the generated scene graph is used to plan the ego vehicle’s trajectory, by predicting movements of the detected dynamic objects near the ego vehicle.
and control the ego-vehicle based on the predicted future motion of the at least one traffic participant, wherein to control the ego-vehicle includes to execute at least one of a steering function of the ego-vehicle, an acceleration of the ego-vehicle, or a deceleration of the ego-vehicle. See at least col. 7 lines 10 – 47, col. 8 lines 25 – 42, col. 21 line 45 – col. 22 line 15, and figure 10 (step 1010), wherein the generated scene graph is used to plan the ego vehicle’s trajectory, by predicting movements of the detected dynamic objects near the ego vehicle. The vehicle’s braking/steering/acceleration subsystems are then controlled by a motion control directive based on the planned trajectory.
Lukarski remains silent as to the specifics of generating a knowledge graph, wherein to organize includes to organize the nodes of the knowledge graph with respect to scenes and sequences of the environment sensor data and the map data; and wherein a sequence represents an ordered collection of scenes.
Al Faruque teaches generating a knowledge graph. See at least [0005], [0022] and figure 3, wherein the generated scene graph is a knowledge graph that encodes knowledge about a visual scene.
, wherein to organize includes to organize the nodes of the knowledge graph with respect to scenes and sequences of the environment sensor data and the map data. See at least [0118] and figure 7, wherein the sensor data is organized into sequences (video clips) of successive scenes (image frame) during sensor data preprocessing. In combination with Lukarski’s teaching, discussed above, of environment sensor data and map data, this limitation is taught in its entirety.
and wherein a sequence represents an ordered collection of scenes. See at least [0116]-[0118], wherein a sequence represents an ordered collection of image frames, or scenes. Per pg. 23, lines 23-25 of Applicant’s Specification, a sequence 537 is equivalent to a video where its frames are scenes 533/535.
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to modify Lukarski with Al Faruque’s knowledge graph. It would have been obvious to modify because doing so enables models to assess the risk of driving maneuvers with better performance, as recognized by Al Faruque (see at least [0022] and [0027]-[0029]).
Regarding claim 15, Lukarski teaches A non-transitory computer-readable storage medium on which is stored a computer program including instructions for generating a knowledge graph for traffic motion prediction. See at least figures 1 and 10. Additionally, see at least figure 11 (main memory 9020).
the instructions, when executed by a data processor, causing the data processor to perform the following steps: receiving environment sensor data of at least one environment sensor of an ego-vehicle, wherein the environment sensor data represent an environment of the ego-vehicle and include information regarding at least one traffic participant located in the environment of the ego-vehicle. See at least col. 4 line 35 – col. 5 line 7, col. 8 line 43 – col. 9 line 22, col. 21 lines 1 – 18, figure 1 (block 112), and figure 10 (step 1001), wherein sensors of a vehicle collect sensor data regarding an environment of the ego vehicle 110. The sensor data includes data on static and dynamic objects in the environment surrounding the vehicle. See at least col. 13 lines 9 – 33, wherein dynamic objects include traffic participants such as cars, buses, trucks, motorcycles, trains, bicycles, pedestrians, etc.
receiving map data from an electronic road map, wherein the map data represent a road network in the environment of the ego-vehicle and include information regarding at least one motion track the at least one traffic participant is positioned on. See at least col. 4 line 35 – col. 5 line 7, col. 7 lines 48 – 64, col. 10 line 50 – col. 11 line 16, col. 12 line 22 – col. 13 line 8, col. 21 lines 1 – 18, figure 1 (blocks 114 and 175), figure 2, figure 3 (map(s) 374), and figure 10 (step 1001), wherein map data is received via communication devices from external sources. The map data represents static objects in the environment of the ego-vehicle such as lane segments, and includes information on static objects related to dynamic objects (traffic participants) in the environment.
extracting the information regarding the at least one traffic participant from the environment sensor data. See at least col. 8 line 43 – col. 9 line 22 and figure 1 (block 165), wherein analyzers extract object identification information from the environment sensor data.
and extracting the information regarding the at least one motion track the at least one traffic participant is positioned on from the map data. See at least col. 12 line 22 – col. 13 line 8, col. 19 line 22 – col. 20 line 67, and figure 9, wherein information on static objects associated with detected dynamic objects is obtained from map data 952.
generating a graph of the road network in the environment of the ego-vehicle including nodes and edges based on the map data and/or the environment sensor data. See at least col. 4 line 35 – col. 5 line 7, col. 21 lines 19 – 35, figure 1 (block 115), and figure 10 (step 1004), wherein a unified scene graph is created, representing the environment of the vehicle and including nodes and edges based on the received map and sensor data.
wherein the graph includes at least one node representing the at least one traffic participant, and at least one node representing the at least one motion track the at least one traffic participant is positioned on. See at least col. 15 line 28 – 67 and figure 6, wherein the graph 610 includes nodes P12 (pedestrian) and OV11 (other vehicle) representing traffic participants, and additionally includes nodes LS2 and LD8, representing the lane segments that the pedestrian and other vehicle, respectively, are located on.
organizing the nodes of the knowledge graph into classes and sub-classes, wherein the sub-classes include at least one node representing a specific type of the at least one traffic participant selected from the group consisting of a static object, a movable object, a vehicle, an animal, and a human. See at least col. 12 line 22 – col. 13 line 33 and figure 3, wherein a hierarchy of sub-categories are defined for the categories. For example, the category of other vehicles 373 is organized into sub-categories including cars, buses, trucks, motorcycles, trains, bicycles, etc.
wherein a scene includes information of the environment sensor data and the map data for one time stamp. See at least col. 5 lines 8 – 38, wherein the graph is organized into scenes, by including multiple nodes representing objects at particular time stamps. Edges are formed in the graph to indicate succession. For example, a tracked object in the scene at time T1 is represented by node N1, and the same object in the scene at time T2 is represented by node N2. The two nodes form the sequence N1 – μl – N2, wherein μl is used to represent the dynamics of the scene between T1 and T2.
predicting a future motion of the at least one traffic participant positioned in the environment of the ego-vehicle based on the knowledge graph. See at least col. 7 lines 10 – 47, col. 8 lines 25 – 42, col. 21 line 45 – col. 22 line 3, and figure 10 (step 1010), wherein the generated scene graph is used to plan the ego vehicle’s trajectory, by predicting movements of the detected dynamic objects near the ego vehicle.
and controlling the ego-vehicle based on the predicted future motion of the at least one traffic participant, the controlling including executing at least one of a steering function of the ego-vehicle, an acceleration of the ego-vehicle, or a deceleration of the ego-vehicle. See at least col. 7 lines 10 – 47, col. 8 lines 25 – 42, col. 21 line 45 – col. 22 line 15, and figure 10 (step 1010), wherein the generated scene graph is used to plan the ego vehicle’s trajectory, by predicting movements of the detected dynamic objects near the ego vehicle. The vehicle’s braking/steering/acceleration subsystems are then controlled by a motion control directive based on the planned trajectory.
Lukarski remains silent as to the specifics of generating a knowledge graph, wherein the organizing further includes organizing the nodes of the knowledge graph with respect to scenes and sequences of the environment sensor data and the map data; and wherein a sequence represents an ordered collection of scenes.
Al Faruque teaches generating a knowledge graph. See at least [0005], [0022] and figure 3, wherein the generated scene graph is a knowledge graph that encodes knowledge about a visual scene.
wherein the organizing further includes organizing the nodes of the knowledge graph with respect to scenes and sequences of the environment sensor data and the map data. See at least [0118] and figure 7, wherein the sensor data is organized into sequences (video clips) of successive scenes (image frame) during sensor data preprocessing. In combination with Lukarski’s teaching, discussed above, of environment sensor data and map data, this limitation is taught in its entirety.
and wherein a sequence represents an ordered collection of scenes. See at least [0116]-[0118], wherein a sequence represents an ordered collection of image frames, or scenes. Per pg. 23, lines 23-25 of Applicant’s Specification, a sequence 537 is equivalent to a video where its frames are scenes 533/535.
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to modify Lukarski with Al Faruque’s knowledge graph. It would have been obvious to modify because doing so enables models to assess the risk of driving maneuvers with better performance, as recognized by Al Faruque (see at least [0022] and [0027]-[0029]).
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.
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/S.M.J./ Examiner, Art Unit 3667
/FARIS S ALMATRAHI/ Supervisory Patent Examiner, Art Unit 3667
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