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
Claims 1, 3-6, 11, and 13-16 are pending. Claims 2, 7-10, 12, and 17-20 are canceled.
Response to Arguments
Applicant’s arguments, see p.9-13, filed 03/03/2026, with respect to the rejection of Claims 1-20 under 35 U.S.C. 101 have been fully considered and are persuasive. Therefore, the rejection of Claims 1-20 under this section of the Rules has been withdrawn.
Applicant’s arguments, see p.13-19, filed 03/03/2026, with respect to the rejections of Claims 1-20 under 35 U.S.C. 103 have been fully considered but are moot because Applicant’s amendments of the independent claims has altered the scope of the claims, and therefore, necessitated new grounds of rejection which are presented below. Accordingly, THIS ACTION IS MADE FINAL.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 3-6, 11, and 13-16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation "…in response to the determining…" and Claim 11 recites the limitation “…in response to the determination…”. There is insufficient antecedent basis for these limitations in the claims.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 3, 4, 11, 13, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Chattopadhyay et al. (US 20240151855 A1) in view of Qian et al. (US 20200126239 A1), Armstrong-Crews et al. (US 20220135074 A1), Ishii (US 20230020725 A1), Evans et al. (US 20220101020 A1), and Li et al. (“A multiple object tracking method using Kalman filter”).
Regarding Claim 1, Chattopadhyay teaches "A method of tracking objects detected through light detection and ranging (LiDAR) points to maintain tracking continuity during temporary grouping of objects"; (Chattopadhyay, Abstract and Paras. 23 and 82, teaches a LiDAR-based tracking method based on point cloud data wherein a similar segment is tracked from the first frame to the second frame based on object identification in which it is capable of detecting objects in every frame and a feature-based matching method to track the objects one frame to another, i.e., method of tracking objects detected through LiDAR points to maintain tracking continuity during grouping of objects);
"the method comprising: receiving, by a processor from a LiDAR sensor mounted on a vehicle, LiDAR points representing a physical environment"; (Chattopadhyay, Paras. 15 and 17, teaches vehicles may include forward, sideward, and rearward facing sensors including LiDAR wherein the sensor signals help generate a map of the environment around a vehicle, i.e., LiDAR sensor mounted on a vehicle to represent a physical environment);
"segmenting, by the processor, the LiDAR points in a previous frame to recognize two or more objects"; (Chattopadhyay, Paras. 82-83, teaches a method for tracking objects by filtering LiDAR point cloud data to remove ground returns, returns from objects not on the road, and objects behind the vehicle and performing segmentation on LiDAR point cloud data using a density-based clustering algorithm wherein a successive frame is similarly processed to identify segments or clusters wherein the segmented data includes a first plurality of segments in a first frame and a second plurality of segments in a second frame, i.e., recognize a plurality of objects by segmenting the LiDAR points in at least a previous frame);
"and storing, in a memory, for each of the two or more objects, an object ID and a center point position"; (Chattopadhyay, Paras. 71-73, teaches object IDs and location of an object may be stored in an object identification database wherein the distance may be measured from the center of a cluster to the center of other clusters, i.e., storing object IDs and positions of objects in memory wherein position of the object includes its center);
"";
"";
"";
"calculating center points of the plurality of clusters in the current frame"; (Chattopadhyay, Para. 73, teaches measuring distance from the center of a given cluster to the center of other clusters, i.e., calculate center points of the plurality of clusters in the frame).
However, Chattopadhyay does not explicitly teach "classifying, by the processor, the two or more objects recognized in the previous frame as one classified object in a current frame based on spatial proximities of the LiDAR points; in response to the determining, re-segmenting only the LiDAR points associated with the one classified object in the current frame into a plurality of clusters using a K-means clustering algorithm; wherein a number of the plurality of clusters is set equal to a number of the two or more objects recognized in the previous frame; matching the stored center point positions of the two or more objects in the previous frame with the calculated center points of the plurality of clusters by computing Euclidean distances and identifying shortest distances; updating the stored center point positions in the memory according to the matching while maintaining the object IDs of the two or more objects as separate tracking entities during the current frame in which the two or more objects are classified as the one classified object; and when the one classified object is subsequently classified into the two or more objects in a next frame, assigning to the two or more objects the maintained object IDs based on the updated center point positions to preserve tracking history and reduce tracking loss caused by temporary merging of LiDAR points".
In an analogous field of endeavor, Qian teaches "classifying, by the processor, the two or more objects recognized in the previous frame as one classified object in a current frame based on spatial proximities of the LiDAR points"; (Qian, FIGs. 4 and 8-9 and Paras, 45 and 50, teaches processing an image frame to identify a plurality of targets which may be stationary or moving in order to track the targets wherein LIDAR data is used to create the images, i.e., classify LiDAR points into two or more objects of a previous frame, and wherein the target objects are selected as part of a target group which has a state for considered the positions and velocity of the target objects, i.e., classifying the two or more target objects as one object in the current frame being the grouping of the target objects as one target group which is tracked).
It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Chattopadhyay by including the classification of two or more objects in a previous frame as one classified object in a current frame based on spatial proximity of LiDAR points taught by Qian. One of ordinary skill in the art would be motivated to combine the references since it accurately tracks multiple objects (Qian, Paras. 2-3, teaches the motivation of combination to be to accurately track multiple target objects as a target group).
However, the combination of references of Chattopadhyay in view of Qian does not explicitly teach “in response to the determining, re-segmenting only the LiDAR points associated with the one classified object in the current frame into a plurality of clusters using a K-means clustering algorithm; wherein a number of the plurality of clusters is set equal to a number of the two or more objects recognized in the previous frame; matching the stored center point positions of the two or more objects in the previous frame with the calculated center points of the plurality of clusters by computing Euclidean distances and identifying shortest distances; updating the stored center point positions in the memory according to the matching while maintaining the object IDs of the two or more objects as separate tracking entities during the current frame in which the two or more objects are classified as the one classified object; and when the one classified object is subsequently classified into the two or more objects in a next frame, assigning to the two or more objects the maintained object IDs based on the updated center point positions to preserve tracking history and reduce tracking loss caused by temporary merging of LiDAR points".
In an analogous field of endeavor, Armstrong-Crews teaches "in response to the determining, re-segmenting only the LiDAR points associated with the one classified object in the current frame into a plurality of clusters using a K-means clustering algorithm"; (Armstrong-Crews, Paras. 22, 32, and 38, teaches classification of objects can be facilitated by fitting the coordinates and the radial velocity of various clusters representing parts of a composite object which are extracted by coherent lidar sensors wherein the cluster of points can be segmented into two or more sub-clusters with each sub-cluster corresponding to various parts of the composite object and wherein segmenting a cluster that corresponds to a composite object into sub-clusters can be performed by various clustering methods such as K-means clustering, i.e., an object cluster of LiDAR points may be segmented into a plurality of clusters using a K-means clustering in response to an object being classified as one object).
It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Chattopadhyay and Qian by including the segmentation LiDAR points of the one object into a plurality of clusters using a K-means clustering algorithm taught by Armstrong-Crews. One of ordinary skill in the art would be motivated to combine the references since it improves autonomous driving (Armstrong-Crews, Para. 1, teaches the motivation of combination to be to improve autonomous driving systems by assisting in classification).
However, the combination of references of Chattopadhyay in view of Qian and Armstrong-Crews does not explicitly teach “wherein a number of the plurality of clusters is set equal to a number of the two or more objects recognized in the previous frame; matching the stored center point positions of the two or more objects in the previous frame with the calculated center points of the plurality of clusters by computing Euclidean distances and identifying shortest distances; updating the stored center point positions in the memory according to the matching while maintaining the object IDs of the two or more objects as separate tracking entities during the current frame in which the two or more objects are classified as the one classified object; and when the one classified object is subsequently classified into the two or more objects in a next frame, assigning to the two or more objects the maintained object IDs based on the updated center point positions to preserve tracking history and reduce tracking loss caused by temporary merging of LiDAR points".
In an analogous field of endeavor, Ishii teaches "wherein a number of the plurality of clusters is set equal to a number of the two or more objects recognized in the previous frame"; (Ishii, Para. 55, teaches the point cloud to be clustered in the generated point cloud data and the number of clusters are determined on the basis of the estimated number of objects, i.e., clustering the points in the current frame into the plurality of clusters equal to the number of objects counted in the previous frame).
It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Chattopadhyay, Qian, and Armstrong-Crews wherein the points are LiDAR points in a current frame corresponding to points in a previous frame by including the number of clusters is equal to a number of objects in the previous frame taught by Ishii. One of ordinary skill in the art would be motivated to combine the references since it improves efficiency and accuracy (Ishii, Abstract, teaches the motivation of combination to be to improve the efficiency and accuracy of clustering).
However, the combination of references of Chattopadhyay in view of Qian, Armstrong-Crews, and Ishii does not explicitly teach “matching the stored center point positions of the two or more objects in the previous frame with the calculated center points of the plurality of clusters by computing Euclidean distances and identifying shortest distances; updating the stored center point positions in the memory according to the matching while maintaining the object IDs of the two or more objects as separate tracking entities during the current frame in which the two or more objects are classified as the one classified object; and when the one classified object is subsequently classified into the two or more objects in a next frame, assigning to the two or more objects the maintained object IDs based on the updated center point positions to preserve tracking history and reduce tracking loss caused by temporary merging of LiDAR points".
In an analogous field of endeavor, Evans teaches "matching the stored center point positions of the two or more objects in the previous frame with the calculated center points of the plurality of clusters by computing Euclidean distances and identifying shortest distances"; (Evans, FIG. 2D and Para. 28, teaches determining a sufficient similarity between an object in a first image and an object in a second image based on a smallest distance between the first vector and any other vector associated with objects in the second image wherein distance is measured between the center points of the regions of interest, i.e., calculate a distance between each of the center points of the objects in the previous frame and each of the center points of the clusters in the current frame and the matching points have the shortest distances among the calculated distance).
It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Chattopadhyay, Qian, Armstrong-Crews, and Ishii wherein objects are detected by clusters of points by including the matching of object positions in a previous frame to objects in the current frame by computing a distance and identifying shortest distances taught by Evans. One of ordinary skill in the art would be motivated to combine the references since it improves accuracy and safety (Evans, Abstract, teaches the motivation of combination to be to improve the accuracy and safety of the system).
However, the combination of references of Chattopadhyay in view of Qian, Armstrong-Crews, Ishii, and Evans does not explicitly teach “updating the stored center point positions in the memory according to the matching while maintaining the object IDs of the two or more objects as separate tracking entities during the current frame in which the two or more objects are classified as the one classified object; and when the one classified object is subsequently classified into the two or more objects in a next frame, assigning to the two or more objects the maintained object IDs based on the updated center point positions to preserve tracking history and reduce tracking loss caused by temporary merging of LiDAR points".
In an analogous field of endeavor, Li teaches "updating the stored center point positions in the memory according to the matching while maintaining the object IDs of the two or more objects as separate tracking entities during the current frame in which the two or more objects are classified as the one classified object"; (Li, FIGs. 1 and 3-4 and Section II-B and Section III, teaches the objects geometric features include location and center of mass or centroid wherein a Kalman filter can be used to estimate the object's location and to gain trajectories of moving objects wherein frame features are used to update parameters of the Kalman filter and use them as the input in the next frame wherein two moving objects TA and TB occlusion occurred in time k1, they merged into to one object T in time k2, starting from time k2, object T will be tracked as a new moving object, and during time k2 to time k3, object TA and TB also will be updated as well while updating object T, i.e., updating center positions of objects while maintaining the object IDs of the two or more objects as separate tracking entities during the current frame in which the two or more objects are classified as the one classified object);
"and when the one classified object is subsequently classified into the two or more objects in a next frame, assigning to the two or more objects the maintained object IDs based on the updated center point positions to preserve tracking history and reduce tracking loss caused by temporary merging of LiDAR points"; (Li, FIGs. 1 and 3-4 and Section III and Section IV-B, teaches object T' splits into two objects, T'A and T'B in which objects TA and TB are matched with T'A and T'B in a certain range of objects' locations and establishing and updating the correspondence and wherein the information is sent to their associated trackers to maintain correct ID for tracking after multi-person object separation and the tracking results show that the Kalman filter based tracker does a good job of tracking the human bodies when they get partially or completely occluded, even in the difficult case of simultaneous merge and split, i.e., one classified object is subsequently classified into two or more objects in a next frame and assigning the two or more objects the maintained object IDs based on the updated center positions to preserve tracking history and loss from temporary merging).
It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Chattopadhyay, Qian, Armstrong-Crews, Ishii, and Evans wherein the object positions are determined by LiDAR points by including the updating of object positions while maintaining object IDs of the objects during the classification of the objects as one object and classifying the one object into the two or more objects gain in the next frame by maintaining the object IDs based on the updated positions taught by Li. One of ordinary skill in the art would be motivated to combine the references since it improves tracking efficiency in confusing situations (Li, Abstract, teaches the motivation of combination to be to achieve efficient tracking of multiple moving objects under confusing situations).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date.
Regarding Claim 3, the combination of references of Chattopadhyay in view of Qian, Armstrong-Crews, Ishii, Evans, and Li teaches "The method of claim 1, wherein the spatial proximities of LiDAR points are calculated between the one classified object classified in the current frame and each of the two or more objects in the previous frame"; (Chattopadhyay, Paras. 71 and 73, teaches computing similarity between an object in a first frame to all objects within the neighborhood in a successive frame wherein object similarity uses a neighborhood radius for each object when comparing to possible clusters in successive frames to track the object in which the neighborhood radius for an object is measured based on the maximum distance of an object cluster from all other cluster centers in the next frame, i.e., calculate similarity scores or spatial proximities between object classified in a current frame and each of the objects in a previous frame).
Regarding Claim 4, the combination of references of Chattopadhyay in view of Qian, Armstrong-Crews, Ishii, Evans, and Li teaches "The method of claim 3, further including assigning an ID of the object in the previous frame corresponding to the highest spatial proximity as an ID of the one classified object classified in the current frame"; (Chattopadhyay, Para. 71 and 73, teaches objects with the maximum similarity are assigned the same Object ID from frame-to-frame wherein object similarity uses a neighborhood radius for each object when comparing to possible clusters in successive frames to track the object in which the neighborhood radius for an object is measured based on the maximum distance of an object cluster from all other cluster centers in the next frame, i.e., assigning the ID of the object in the previous frame corresponding to the highest similarity score or proximity as an ID of the object classified in the current frame).
Claim 11 recites a device with elements corresponding to the steps recited in Claim 1. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Chattopadhyay in view of Qian, Armstrong-Crews, Ishii, Evans, and Li references, presented in rejection of Claim 1, apply to this claim. Finally, the combination of the Chattopadhyay in view of Qian, Armstrong-Crews, Ishii, Evans, and Li references discloses a processor and a memory for storing and executing instructions (for example, see Chattopadhyay, Paragraph 91).
Claim 13 recites a device with elements corresponding to the steps recited in Claim 3. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Chattopadhyay in view of Qian, Armstrong-Crews, Ishii, Evans, and Li references, presented in rejection of Claim 3, apply to this claim. Finally, the combination of the Chattopadhyay in view of Qian, Armstrong-Crews, Ishii, Evans, and Li references discloses a processor and a memory for storing and executing instructions (for example, see Chattopadhyay, Paragraph 91).
Claim 14 recites a device with elements corresponding to the steps recited in Claim 4. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Chattopadhyay in view of Qian, Armstrong-Crews, Ishii, Evans, and Li references, presented in rejection of Claim 3, apply to this claim. Finally, the combination of the Chattopadhyay in view of Qian, Armstrong-Crews, Ishii, Evans, and Li references discloses a processor and a memory for storing and executing instructions (for example, see Chattopadhyay, Paragraph 91).
Claims 5-6 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Chattopadhyay in view of Qian, Armstrong-Crews, Ishii, Evans, Li, and Matsubara et al. (US 20200097501 A1).
Regarding Claim 5, the combination of references of Chattopadhyay in view of Qian, Armstrong-Crews, Ishii, Evans, and Li teaches "The method of claim 3, further including assigning a first sub-ID to the object in the previous frame corresponding to the highest spatial proximity among the spatial proximities in the current frame"; (Chattopadhyay, Paras. 71 and 73, teaches an object similarity calculator which computes similarity between an object in a first frame to all objects within the neighborhood in a successive frame wherein objects with the maximum similarity are assigned the same Object ID from frame-to-frame wherein object similarity uses a neighborhood radius for each object when comparing to possible clusters in successive frames to track the object in which the neighborhood radius for an object is measured based on the maximum distance of an object cluster from all other cluster centers in the next frame, i.e., assigning the same ID or sub-ID to an object in the previous frame with the highest similarity score or proximity among the similarity scores or proximities in the current frame).
However, the combination of references of Chattopadhyay in view of Qian, Armstrong-Crews, Ishii, Evans, and Li does not explicitly teach "and assigning a second sub-ID to the object in the previous frame corresponding to the remaining spatial proximities excluding the highest spatial proximity among the spatial proximities in the current frame".
In an analogous field of endeavor, Matsubara teaches "and assigning a second sub-ID to the object in the previous frame corresponding to the remaining spatial proximities excluding the highest spatial proximity among the spatial proximities in the current frame"; (Matsubara, Paras. 60-62, teaches a list of moving object IDs assigned to the same tracking ID wherein a method for tracking a moving object is one in which image feature values are extracted from an image having a moving object ID of “1” detected in a given frame and an image having a moving object ID of “4” detected in another frame wherein a degree of similarity is calculated between the image feature values, i.e., a second sub-ID is assigned to the object in the previous frame corresponding to a plurality of degrees of similarity and not just the highest similarity score).
It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Chattopadhyay in view of Qian, Armstrong-Crews, Ishii, Evans, and Li wherein remaining similarity score is calculated that excludes a highest similarity score and wherein similarity score is computed using a neighborhood radius for each object by including the assigning of an ID to the object in a previous frame that corresponds to a remaining similarity score that does not include a highest score taught by Matsubara. One of ordinary skill in the art would be motivated to combine the references since it eases tracking (Matsubara, Para. 8, teaches the motivation of combination to be to improve the coverage of the search for the moving object and ease the tracking of the object).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date.
Regarding Claim 6, the combination of references of Chattopadhyay in view of Qian, Armstrong-Crews, Ishii, Evans, Li, and Matsubara teaches "The method of claim 5, wherein the first sub-ID includes an ID of the object in the previous frame corresponding to the highest spatial proximity among the spatial proximities"; (Chattopadhyay, Para. 71 and 73, teaches an object similarity calculator which computes similarity between an object in a first frame to all objects within the neighborhood in a successive frame wherein objects with the maximum similarity are assigned the same Object ID from frame-to-frame wherein object similarity uses a neighborhood radius for each object when comparing to possible clusters in successive frames to track the object in which the neighborhood radius for an object is measured based on the maximum distance of an object cluster from all other cluster centers in the next frame, i.e., assigning the same ID or sub-ID to an object in the previous frame as the object in the current frame with the highest similarity score or proximity among the similarity scores or proximities);
"and the second sub-ID includes an ID of the object in the previous frame corresponding to the remaining spatial proximities excluding the highest spatial proximity among the spatial proximities"; (Matsubara, Paras. 60-62, teaches a list of moving object IDs assigned to the same tracking ID wherein a method for tracking a moving object is one in which image feature values are extracted from an image having a moving object ID of “1” detected in a given frame and an image having a moving object ID of “4” detected in another frame wherein a degree of similarity is calculated between the image feature values, i.e., a second sub-ID is assigned to the object in the previous frame and includes the ID of the object being the Track ID and corresponding to a plurality of degrees of similarity and not just the highest similarity score).
The proposed combination as well as the motivation for combining the Chattopadhyay in view of Qian, Armstrong-Crews, Ishii, Evans, Li, and Matsubara references presented in the rejection of Claim 5, applies to claim 6. Thus, the method recited in claim 6 is met by Chattopadhyay in view of Qian, Armstrong-Crews, Ishii, Evans, Li, and Matsubara.
Claim 15 recites a device with elements corresponding to the steps recited in Claim 5. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Chattopadhyay in view of Qian, Armstrong-Crews, Ishii, Evans, Li, and Matsubara references, presented in rejection of Claim 5, apply to this claim. Finally, the combination of the Chattopadhyay in view of Qian, Armstrong-Crews, Ishii, Evans, Li, and Matsubara references discloses a processor and a memory for storing and executing instructions (for example, see Chattopadhyay, Paragraph 91).
Claim 16 recites a device with elements corresponding to the steps recited in Claim 6. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Chattopadhyay in view of Qian, Armstrong-Crews, Ishii, Evans, Li, and Matsubara references, presented in rejection of Claim 5, apply to this claim. Finally, the combination of the Chattopadhyay in view of Qian, Armstrong-Crews, Ishii, Evans, Li, and Matsubara references discloses a processor and a memory for storing and executing instructions (for example, see Chattopadhyay, Paragraph 91).
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 extension fee 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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/ANDREW S BUDISALICH/Examiner, Art Unit 2662
/AMANDEEP SAINI/Supervisory Patent Examiner, Art Unit 2662
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