DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. 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
2. This office action is in response to Amendments and Remarks filed on 02/18/2026 for application number 18/048,011 filed on 10/19/2022, in which claims 1-20 were previously presented for examination.
3. Claim(s) 21-23 has/have been added as new, claim(s) 7, 15, and 20 has/have been canceled, and claim(s) 1, 9, and 17 has/have been amended. Accordingly, claim(s) 1-6, 8-14, 16-19, and 21-12 is/are currently pending.
Priority
4. Acknowledgment is made of Applicant’s claim for priority of provisional application No. 16/401,772.
Examiner Notes
5. The Examiner has cited particular paragraphs or columns and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested of the applicant in preparing responses, to fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner. The prompt development of a clear issue requires that the replies of the Applicant meet the objections to and rejections of the claims. Applicant should also specifically point out the support for any amendments made to the disclosure (see MPEP §2163.06). Applicant is reminded that the Examiner is entitled to give the Broadest Reasonable Interpretation (BRI) of the language of the claims. Furthermore, the Examiner is not limited to Applicant’s definition which is not specifically set forth in the claims. SEE MPEP 2141.02 [R-07.2015] VI. PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, INCLUDING DISCLOSURES THAT TEACH AWAY FROM THE CLAIMS: A prior art reference must be considered in its entirety, i.e., as a whole, including portions that would lead away from the claimed invention. W.L. Gore & Associates, Inc. v. Garlock, Inc., 721 F.2d 1540, 220 USPQ 303 (Fed. Cir. 1983), cert, denied, 469 U.S. 851 (1984). See also MPEP §2123.
Response to Arguments
6. Applicant's arguments filed 02/18/2026 have been fully considered but they are not persuasive.
Rejections under 35 U.S.C. § 101
7. Applicant’s argument in regard to the rejection of the claims under 35 U.S.C. § 101 is addressed in Claim Rejections - 35 USC § 101 section below.
Rejections under 35 U.S.C. § 103
8. Applicant argues the amended claim(s) 1 is/are allowable over Silver et al. (US-9285230-B1) and the other cited references. Applicant argues, the cited references fail to teach or suggest the following amended features: “wherein the curb of the road is determined by applying curve fitting to detected points corresponding to the curb; wherein detected points corresponding to an obstruction that occludes the curb of the road are removed from consideration for curve fitting.”
9. Indeed, these references do not teach the newly amended feature(s) above. As such, this amendment has necessitated additional reference Non-patent Literature Hata et al. (“Robust Curb Detection and Vehicle Localization in Urban Environments”). Applicant is referred to the new ground of rejection outlined in Claim Rejections - 35 USC § 103 section below.
10. As such, Silver, in view of Hata, teaches each and every limitation of these claims and this argument is moot.
11. Applicant argues independent claim(s) 9, and 17 has/have been amended similar to independent claim 1 and it/they is/are allowable for reasons similar to those presented in favor of patentability of claim 1.
12. This argument is unpersuasive as each independent claim has been fully rejected and for the reasons given above.
13. Applicant argues dependent claim(s) is/are patentable by the virtue of their dependency on one of the independent claims and the additional features recited in the dependent claims.
14. This argument is unpersuasive as each independent claim and dependent claim has been fully rejected and for the reasons given above.
Claim Rejections - 35 USC § 101
15. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
16. Claim(s) 1-6, 8-14, 16-19, and 21-23 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
17. The determination of whether a claim recites patent ineligible subject matter is a 2 step inquiry.
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), see MPEP 2106.03, or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis: see MPEP 2106.04
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon? see MPEP 2106.04(II)(A)(1)
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? see MPEP 2106.04(II)(A)(2) and 2106.05(a) thru (d) for explanations.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? see MPEP 2106.05
101 Analysis – Step 1
18. Claim(s) 1-6, 8, and 21-23 is/are directed to a method (i.e. a process).
Therefore, claim(s) 1-6, 8, and 21-23 is/are within at least one of the four statutory categories.
19. Claim(s) 9-14, and 16 is/are directed to an apparatus.
Therefore, claim(s) 9-14, and 16 is/are within at least one of the four statutory categories.
20. Claim(s) 17-19 is/are directed to a non-transitory computer-readable medium (i.e. an article of manufacture).
Therefore, claim(s) 17-19 is/are within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
21. Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes. see MPEP 2106(A)(II)(1) and MPEP 2106.04(a)-(c).
22. Independent claim(s) 1, 9, and 17 include(s) limitations that recite an abstract idea (emphasized below [with the category of abstract idea in brackets]). Claim 9 will be used as a representative claim for the remainder of the 101 rejection. Claim 9 recites:
An apparatus for detecting road features, comprising:
a processor; and
a memory comprising executable code that, when executed by the processor, causes the apparatus to:
obtain a point-cloud frame that comprises a description of an intensity of a reflection of beams from an area around a vehicle;
create a plurality of clusters that each include (i) one or more seed points of the point- cloud frame, and (ii) additional points of the point-cloud frame based on a relationship between the additional points and the one or more seed points [mental process/step];
identify a cluster from the plurality of clusters based on a total number of points included in each of the plurality of clusters [mental process/step]; and
detect a road feature from the cluster:
wherein detecting the road feature comprises detecting, according to a boundary of the cluster, a curb of a road, wherein the vehicle is located on the road [mental process/step];
wherein the curb of the road is determined by applying curve fitting to detected points corresponding to the curb [mental process/step];
wherein detected points corresponding to an obstruction that occludes the curb of the road are removed from consideration for curve fitting [mental process/step].
23. The Examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers steps that could be carried out in the human mind. For example, “create a plurality of clusters ...,” “identify a cluster from the plurality of clusters ...,” “detect a road feature from the cluster: wherein detecting the road feature comprises detecting, according to a boundary of the cluster, a curb of a road, wherein the vehicle is located on the road, ” “wherein the curb of the road is determined by applying curve fitting to detected points corresponding to the curb,” and “wherein detected points corresponding to an obstruction that occludes the curb of the road are removed from consideration for curve fitting,” step(s) encompass(es) a user making observation, evaluation or judgement about a road features, could all be carried out in one’s mind. The same user looking at the data collected, could form a simple judgement and conclude whether a road feature is detected in the received data by curve fitting and removing the obstruction points. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
24. Regarding Prong II of the Step 2A analysis, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. see MPEP 2106.04(II)(A)(2) and MPEP 2106.04(d)(2). It must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
25. In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” [with a description of the additional limitations in brackets], while the bolded portions continue to represent the “abstract idea”.):
An apparatus for detecting road features, comprising [generic linking to technical field, 2106.05(h)]:
a processor [applying the abstract idea using generic computing module, Apply it 2106.05(f)]; and
a memory comprising executable code that, when executed by the processor, causes the apparatus to [applying the abstract idea using generic computing module, Apply it 2106.05(f)]:
obtain a point-cloud frame that comprises a description of an intensity of a reflection of beams from an area around a vehicle [pre-solution activity (data gathering), 2106.05(g) using generic sensors];
create a plurality of clusters that each include (i) one or more seed points of the point- cloud frame, and (ii) additional points of the point-cloud frame based on a relationship between the additional points and the one or more seed points [mental process/step];
identify a cluster from the plurality of clusters based on a total number of points included in each of the plurality of clusters [mental process/step]; and
detect a road feature from the cluster:
wherein detecting the road feature comprises detecting, according to a boundary of the cluster, a curb of a road, wherein the vehicle is located on the road [mental process/step];
wherein the curb of the road is determined by applying curve fitting to detected points corresponding to the curb [mental process/step];
wherein detected points corresponding to an obstruction that occludes the curb of the road are removed from consideration for curve fitting [mental process/step].
26. For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
27. Regarding the additional limitations of “a processor,” and “a memory ...” the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer (vehicle controller) to perform the process. Lastly, these limitations are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
28. Regarding the additional limitation(s) of “an apparatus for detecting road features.” the Examiner submits that this/these limitation(s) is/are an attempt to generally link additional element(s) to a technological environment.
29. Regarding the additional limitation(s) of “obtain a point-cloud frame ...,” the examiner submits that this/these limitation(s) is/are insignificant extra-solution activities that merely use a computer to perform the process. In particular, the “obtain a point-cloud frame ...,” step(s) is/are recited at a high level of generality (i.e. as a general means of gathering data for use in the evaluating step), and amounts to mere data gathering, which is a form of insignificant extra-solution activity.
30. Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception. see MPEP § 2106.05. Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
31. Regarding Step 2B of the Revised Guidance, representative independent claim 9 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “a processor,” and “a memory ...” using a vehicle controller to perform the evaluating… amounts to nothing more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. As discussed above, in regard to the additional limitations of “obtain a point-cloud frame ...,” the examiner submits that these limitations are insignificant extra-solution activities. Lastly, the additional limitation(s) of “an apparatus for detecting road features” is/are an attempt to generally link additional element(s) to a technological environment.
32. As established above claim 9 is representative of all independent claims and therefore claim(s) 1 and 17 is/are rejected for the same reason.
33. Dependent claim(s) 2-6, 8, 10-14, 16, 18-19, and 21-23 does/do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and do not integrate the judicial exception into a practical application. Therefore, dependent claims 2-6, 8, 10-14, 16, 18-19, and 21-23 are not patent eligible under the same rationale as provided for in the rejection of 9.
34. Therefore, claim(s) 1-6, 8-14, 16-19, and 21-23 is/are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 103
35. 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.
36. Claim(s) 1, 6, 9, 14, 17, 19, and 23
is/are rejected under 35 U.S.C. 103 as being unpatentable over Silver et al. (US-9285230-B1) in view of Non-patent Literature Hata et al. (“Robust Curb Detection and Vehicle Localization in Urban Environments”).
In regard to claim 1
, Silver discloses a method for detecting road features, comprising (Silver, in at least Fig. 3, Col 3, lines 4345, discloses a method designed to detect and locate road curbs and/or other barriers [i.e., detecting road features] in the vehicle's environment):
obtaining a point-cloud frame that comprises a description of an intensity of a reflection of beams from an area around a vehicle (Silver, in at least Fig. 3, Col 13, lines 6-12, Col 19, lines 59-60, discloses at block 302, the method 300 includes receiving a plurality of point clouds collected in an incremental order [i.e., obtaining a point-cloud frame] as a vehicle navigates a path where each point cloud includes data points that represent the environment of the vehicle at a given timepoint [i.e., an area around a vehicle] and has associated position information indicative of a position of the vehicle at the given timepoint. The computing system determines intensity information associated with laser returns captured by a vehicle sensor [i.e., a description of an intensity of a reflection of beams from an area around a vehicle]);
creating a plurality of clusters that each include (i) one or more seed points of the point-cloud frame, and (ii) additional points of the point-cloud frame based on a relationship between the additional points and the one or more seed points (Silver, in at least Fig. 3, Col 14, lines 22-25, and lines 43-49, discloses at block 304, the method 300 includes, based on respective associated position information of the plurality of point clouds [i.e., one or more seed points of the point-cloud frame], processing, by a computing device, the plurality of point clouds into a dense point cloud representation [i.e., creating a plurality of clusters]. The dense point cloud representation enables the computing system to extract information that is not obtainable using a single point cloud of data, such as road curbs. The computing system formats the data into a dense point cloud representation to better reflect the position and/or orientation of objects in the environment of the vehicle [i.e., additional points of the point-cloud frame based on a relationship between the additional points and the one or more seed points]);
identifying a cluster from the plurality of clusters based on a total number of points included in each of the plurality of clusters (Silver, in at least Col 14, lines 50-54, discloses the computing system processes received point clouds into a dense point cloud representation by accumulating multiple point clouds and processing the point clouds into the representation [i.e., identifying a cluster from the plurality of clusters based on a total number of points included in each of the plurality of clusters]); and
detecting a road feature from the cluster (Silver, in at least Fig. 3, and Col 21, lines 44-47, discloses at block 310, the method 300 further includes, based on an output of the classification system, determining whether the given data points represent one or more road curbs [i.e., detecting a road feature from the cluster] in the environment of the vehicle);
wherein detecting the road feature comprises detecting, according to a boundary of the cluster, a curb of a road, wherein the vehicle is located on the road (Silver, in at least Fig. 4, Col 22, lines 39-45, discloses vehicle 400 uses a sensor or multiple sensors to detect segments of a road curb, such as road curb segment 404 of the road curb 402. Likewise, the vehicle 400 detects segments of multiple road curbs in some instances [i.e., wherein detecting the road feature comprises detecting, according to a boundary of the cluster, a curb of a road, wherein the vehicle is located on the road]. In addition, during operation, the vehicle's sensors detects other objects or boundaries in the environment as well, such as lane boundaries, guard-rails, etc. Examiner notes, as portrayed by Fig. 4, the vehicle detects the curb when the vehicle is located on the road);
Silver is silent on wherein the curb of the road is determined by applying curve fitting to detected points corresponding to the curb;
wherein detected points corresponding to an obstruction that occludes the curb of the road are removed from consideration for curve fitting.
However, Non-patent Literature Hata teaches wherein the curb of the road is determined by applying curve fitting to detected points corresponding to the curb (Non-patent Literature Hata, in at least p. 1257, teaches independently of the sensor adopted in the curb detection task, they are all susceptible to occlusion when considered a typical urban environment. The most common approach to deal with occlusions is by fitting a model (e.g. spline) in the curb model and remove points that not match with the model [i.e., applying curve fitting to detected points corresponding to the curb]. Examiner notes, Spline fitting model is a curve fitting model);
wherein detected points corresponding to an obstruction that occludes the curb of the road are removed from consideration for curve fitting (Non-patent Literature Hata, in at least p. 1257, teaches independently of the sensor adopted in the curb detection task, they are all susceptible to occlusion when considered a typical urban environment. The most common approach to deal with occlusions [i.e., wherein detected points corresponding to an obstruction that occludes the curb of the road] is by fitting a model (e.g. spline) in the curb model and remove points that not match with the model [i.e., detected points … are removed from consideration for curve fitting]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify Silver in view of Non-patent Literature Hata with a reasonable expectation of success, as both inventions are directed to the same field of endeavor – curb detection – and the combination would provide for matching the detected curb information against it to obtain accurate pose estimation, which is a fundamental capability to self-driving cars (Non-patent Literature Hata, see at least p. 1257).
In regard to claim 6
, Silver, as modified by Non-patent Literature Hata, teaches the method of claim 1, wherein detecting the road feature comprises detecting a drivable area for the vehicle from a portion of the area around the vehicle that is spanned by the cluster (Silver, in at least Col 9, lines 39-43, discloses the navigation and pathing system 148 is configured to determine a driving path for the vehicle 100 [i.e., wherein detecting the road feature comprises detecting a drivable area for the vehicle from a portion of the area around the vehicle that is spanned by the cluster]. The navigation and pathing system 148 additionally is configured to update the driving path dynamically while the vehicle 100 is in operation).
In regard to claim 9
, Silver discloses an apparatus for detecting road features, comprising (Silver, in at least Col 1, line 30, discloses systems for detecting road curbs [i.e., an apparatus for detecting road features]):
a processor (Silver, in at least Fig. 1, and Col 5, lines 51-52, discloses the computing device 111 includes a processor 113 [i.e., a processor], and a memory 114); and
a memory comprising executable code that, when executed by the processor, causes the apparatus to (Silver, in at least Fig. 1, and Col 5, lines 51-55, discloses the computing device 111 includes a processor 113, and a memory 114 [i.e., a memory] which includes instructions 115 executable by the processor 113 [i.e., executable code that, when executed by the processor], and also stores map data 116):
obtain a point-cloud frame that comprises a description of an intensity of a reflection of beams from an area around a vehicle (Silver, in at least in at least Fig. 3, Col 13, lines 6-12, Col 19, lines 59-60, discloses at block 302, the method 300 includes receiving a plurality of point clouds collected in an incremental order [i.e., obtain a point-cloud frame] as a vehicle navigates a path where each point cloud includes data points that represent the environment of the vehicle at a given timepoint [i.e., an area around a vehicle] and has associated position information indicative of a position of the vehicle at the given timepoint. The computing system determines intensity information associated with laser returns captured by a vehicle sensor [i.e., a description of an intensity of a reflection of beams from an area around a vehicle]);
create a plurality of clusters that each include (i) one or more seed points of the point- cloud frame, and (ii) additional points of the point-cloud frame based on a relationship between the additional points and the one or more seed points (Silver, in at least Fig. 3, Col 14, lines 22-25, and lines 43-49, discloses at block 304, the method 300 includes, based on respective associated position information of the plurality of point clouds [i.e., one or more seed points of the point-cloud frame], processing, by a computing device, the plurality of point clouds into a dense point cloud representation [i.e., create a plurality of clusters]. The dense point cloud representation enables the computing system to extract information that is not obtainable using a single point cloud of data, such as road curbs. The computing system formats the data into a dense point cloud representation to better reflect the position and/or orientation of objects in the environment of the vehicle [i.e., additional points of the point-cloud frame based on a relationship between the additional points and the one or more seed points]);
identify a cluster from the plurality of clusters based on a total number of points included in each of the plurality of clusters (Silver, in at least Col 14, lines 50-54, discloses the computing system processes received point clouds into a dense point cloud representation by accumulating multiple point clouds and processing the point clouds into the representation [i.e., identify a cluster from the plurality of clusters based on a total number of points included in each of the plurality of clusters]); and
detect a road feature from the cluster (Silver, in at least Fig. 3, and Col 21, lines 44-47, discloses at block 310, the method 300 further includes, based on an output of the classification system, determining whether the given data points represent one or more road curbs [i.e., detecting a road feature from the cluster] in the environment of the vehicle);
wherein detecting the road feature comprises detecting, according to a boundary of the cluster, a curb of a road, wherein the vehicle is located on the road (Silver, in at least Fig. 4, Col 22, lines 39-45, discloses vehicle 400 uses a sensor or multiple sensors to detect segments of a road curb, such as road curb segment 404 of the road curb 402. Likewise, the vehicle 400 detects segments of multiple road curbs in some instances [i.e., wherein detecting the road feature comprises detecting, according to a boundary of the cluster, a curb of a road, wherein the vehicle is located on the road]. In addition, during operation, the vehicle's sensors detects other objects or boundaries in the environment as well, such as lane boundaries, guard-rails, etc. Examiner notes, as portrayed by Fig. 4, the vehicle detects the curb when the vehicle is located on the road);
Silver is silent on wherein the curb of the road is determined by applying curve fitting to detected points corresponding to the curb;
wherein detected points corresponding to an obstruction that occludes the curb of the road are removed from consideration for curve fitting.
However, Non-patent Literature Hata teaches wherein the curb of the road is determined by applying curve fitting to detected points corresponding to the curb (Non-patent Literature Hata, in at least p. 1257, teaches independently of the sensor adopted in the curb detection task, they are all susceptible to occlusion when considered a typical urban environment. The most common approach to deal with occlusions is by fitting a model (e.g. spline) in the curb model and remove points that not match with the model [i.e., applying curve fitting to detected points corresponding to the curb]. Examiner notes, Spline fitting model is a curve fitting model);
wherein detected points corresponding to an obstruction that occludes the curb of the road are removed from consideration for curve fitting (Non-patent Literature Hata, in at least p. 1257, teaches independently of the sensor adopted in the curb detection task, they are all susceptible to occlusion when considered a typical urban environment. The most common approach to deal with occlusions [i.e., wherein detected points corresponding to an obstruction that occludes the curb of the road] is by fitting a model (e.g. spline) in the curb model and remove points that not match with the model [i.e., detected points … are removed from consideration for curve fitting]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify Silver in view of Non-patent Literature Hata with a reasonable expectation of success, as both inventions are directed to the same field of endeavor – curb detection – and the combination would provide for matching the detected curb information against it to obtain accurate pose estimation, which is a fundamental capability to self-driving cars (Non-patent Literature Hata, see at least p. 1257).
In regard to claim 14
, Silver, as modified by Non-patent Literature Hata, teaches the apparatus of claim 9, wherein detecting the road feature comprises detecting a road surface from a portion of the area around the vehicle that is spanned by the cluster (Silver, in at least Col 9, lines 39-43, and Col 19, lines 48-50, discloses the navigation and pathing system 148 is configured to determine a driving path for the vehicle 100 [i.e., wherein detecting the road feature comprises detecting a road surface from a portion of the area around the vehicle that is spanned by the cluster]. The navigation and pathing system 148 additionally is configured to update the driving path dynamically while the vehicle 100 is in operation. The computing system determines that data points representative of a road surface include a high angle of incidence).
In regard to claim 17
, Silver discloses a non-transitory computer-readable medium storing a program that causes a computer to execute a process, the process comprising (Silver, in at least Col 1, lines 53-57, discloses a non-transitory computer readable medium having stored thereon executable instructions [i.e., a non-transitory computer-readable medium storing a program] that, upon execution by a computing device, cause the computing device to perform functions [i.e., that causes a computer to execute a process]):
obtaining a point-cloud frame that comprises a description of an intensity of a reflection of beams from an area around a vehicle (Silver, in at least Fig. 3, Col 13, lines 6-12, Col 19, lines 59-60, discloses at block 302, the method 300 includes receiving a plurality of point clouds collected in an incremental order [i.e., obtaining a point-cloud frame] as a vehicle navigates a path where each point cloud includes data points that represent the environment of the vehicle at a given timepoint [i.e., an area around a vehicle] and has associated position information indicative of a position of the vehicle at the given timepoint. The computing system determines intensity information associated with laser returns captured by a vehicle sensor [i.e., a description of an intensity of a reflection of beams from an area around a vehicle]);
creating a plurality of clusters that each include (i) one or more seed points of the point- cloud frame, and (ii) additional points of the point-cloud frame based on a relationship between the additional points and the one or more seed points (Silver, in at least Fig. 3, Col 14, lines 22-25, and lines 43-49, discloses at block 304, the method 300 includes, based on respective associated position information of the plurality of point clouds [i.e., one or more seed points of the point-cloud frame], processing, by a computing device, the plurality of point clouds into a dense point cloud representation [i.e., create a plurality of clusters]. The dense point cloud representation enables the computing system to extract information that is not obtainable using a single point cloud of data, such as road curbs. The computing system formats the data into a dense point cloud representation to better reflect the position and/or orientation of objects in the environment of the vehicle [i.e., additional points of the point-cloud frame based on a relationship between the additional points and the one or more seed points]);
identifying a cluster from the plurality of clusters based on a total number of points included in each of the plurality of clusters (Silver, in at least Col 14, lines 50-54, discloses the computing system processes received point clouds into a dense point cloud representation by accumulating multiple point clouds and processing the point clouds into the representation [i.e., identify a cluster from the plurality of clusters based on a total number of points included in each of the plurality of clusters]); and
detecting a road feature from the cluster (Silver, in at least Fig. 3, and Col 21, lines 44-47, discloses at block 310, the method 300 further includes, based on an output of the classification system, determining whether the given data points represent one or more road curbs [i.e., detecting a road feature from the cluster] in the environment of the vehicle);
wherein detecting the road feature comprises detecting, according to a boundary of the cluster, a curb of a road, wherein the vehicle is located on the road (Silver, in at least Fig. 4, Col 22, lines 39-45, discloses vehicle 400 uses a sensor or multiple sensors to detect segments of a road curb, such as road curb segment 404 of the road curb 402. Likewise, the vehicle 400 detects segments of multiple road curbs in some instances [i.e., wherein detecting the road feature comprises detecting, according to a boundary of the cluster, a curb of a road, wherein the vehicle is located on the road]. In addition, during operation, the vehicle's sensors detects other objects or boundaries in the environment as well, such as lane boundaries, guard-rails, etc. Examiner notes, as portrayed by Fig. 4, the vehicle detects the curb when the vehicle is located on the road);
Silver is silent on wherein the curb of the road is determined by applying curve fitting to detected points corresponding to the curb;
wherein detected points corresponding to an obstruction that occludes the curb of the road are removed from consideration for curve fitting.
However, Non-patent Literature Hata teaches wherein the curb of the road is determined by applying curve fitting to detected points corresponding to the curb (Non-patent Literature Hata, in at least p. 1257, teaches independently of the sensor adopted in the curb detection task, they are all susceptible to occlusion when considered a typical urban environment. The most common approach to deal with occlusions is by fitting a model (e.g. spline) in the curb model and remove points that not match with the model [i.e., applying curve fitting to detected points corresponding to the curb]. Examiner notes, Spline fitting model is a curve fitting model);
wherein detected points corresponding to an obstruction that occludes the curb of the road are removed from consideration for curve fitting (Non-patent Literature Hata, in at least p. 1257, teaches independently of the sensor adopted in the curb detection task, they are all susceptible to occlusion when considered a typical urban environment. The most common approach to deal with occlusions [i.e., wherein detected points corresponding to an obstruction that occludes the curb of the road] is by fitting a model (e.g. spline) in the curb model and remove points that not match with the model [i.e., detected points … are removed from consideration for curve fitting]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify Silver in view of Non-patent Literature Hata with a reasonable expectation of success, as both inventions are directed to the same field of endeavor – curb detection – and the combination would provide for matching the detected curb information against it to obtain accurate pose estimation, which is a fundamental capability to self-driving cars (Non-patent Literature Hata, see at least p. 1257).
In regard to claim 19
, Silver, as modified by Non-patent Literature Hata, teaches the non-transitory computer-readable medium of claim 17.
Claim 19 recites a non-transitory computer readable medium having substantially the same features of claim 6 above, therefore claim 19 is rejected for the same reasons as claim 6.
In regard to claim 23
, Silver, as modified by Non-patent Literature Hata, teaches the method of claim 1, accordingly the rejection of claim 1 is incorporated.
Further, Non-patent Literature Hata teaches wherein the obstruction comprises another vehicle which is adjacent to the vehicle and is closer to the curb (Non-patent Literature Hata, in at least p. 1259, teaches when obstacles as pedestrians and cars [i.e., wherein the obstruction comprises another vehicle which is adjacent to the vehicle and is closer to the curb, especially when the cars are parked beside a curb and the cars are adjacent to the vehicle] are present in the street, the distance filter will possibly detect them as curbs. These obstacles causes occlusion to the sensor and make difficult to identify actual curbs. The regression filter is introduced to estimate the curb shape and to remove points that do not follow the road model).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify Silver, as already modified by Non-patent Literature Hata, in view of Non-patent Literature Hata with a reasonable expectation of success, as both inventions are directed to the same field of endeavor – curb detection – and detect other cars that are parked beside the curb and match the detected curb information against it to obtain accurate pose estimation, which is a fundamental capability to self-driving cars (Non-patent Literature Hata, see at least p. 1257).
37. Claim(s) 2-3, 5, 10-11, 13 and 18
is/are rejected under 35 U.S.C. 103 as being unpatentable over Silver et al. (US-9285230-B1) in view Non-patent Literature Hata et al. (“Robust Curb Detection and Vehicle Localization in Urban Environments”) and further in view of Crouch et al. (US-20190370614-A1).
In regard to claim 2
, Silver, as modified by Non-patent Literature Hata, teaches the method of claim 1, accordingly the rejection of claim 1 is incorporated.
Silver, as modified by Non-patent Literature Hata, is silent on all claim limitations.
However, Crouch teaches further comprising:
determining that the additional points are related to the one or more seed points based on determining that the additional points are neighboring to the one or more seed points and meet a criterion associated with the one or more seed points (Crouch, in at least Fig. 6B, and [0067], teaches the 3D point cloud is obtained from a 3D scanner. Fig. 6B illustrates a segment 607 of the 3D point cloud 600 of Fig. 6A including a point 601 and nearest neighbor points 605 around the point 601 [i.e., determining that the additional points are related to the one or more seed points based on determining that the additional points are neighboring to the one or more seed points and meet a criterion associated with the one or more seed points]. Examiner notes, as mentioned above, neighbor points, or the additional points, are determined to be in the same cluster. In Fig. 6B, point 601 is the seed point).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify Silver, as already modified by Non-patent Literature Hata, in view of Crouch with a reasonable expectation of success, as both inventions are directed to the same field of endeavor – classifying an object in a point cloud – and include the neighbor points in the same segment and the combination would provide for achieving acceptable range accuracy and detection sensitivity (Crouch, see at least [0004]).
In regard to claim 3
, Silver, as modified by Non-patent Literature Hata and Crouch, teaches the method of claim 2, accordingly the rejection of claim 2 is incorporated.
Further, Crouch teaches wherein the additional points meet the criterion based on having values that are within a tolerance of values of the one or more seed points (Crouch, in at least Fig. 7, and [0097], teaches in step 717, the closest fit is performed between a test input point cloud and the model point clouds associated with the first and second classes, in order to determine whether or not the object should be classified in the first or second class. If the closest fit is too large, e.g., the mean square distance between points in the test input point cloud and points in the model point cloud for a minimum ratio of closest points between the point clouds is above a threshold square distance [i.e., wherein the additional points meet the criterion based on having values that are within a tolerance of values of the one or more seed points], then the object is considered not to belong to the class. If the mean square distance between points in the test input point cloud and points in the model point cloud for the top 90% of closest points between the point clouds is above 2 cm.sup.2, the object is considered not to belong to the class).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify Silver, as already modified by Non-patent Literature Hata and Crouch, further in view of Crouch with a reasonable expectation of success, as both inventions are directed to the same field of endeavor – classifying an object in a point cloud – and include the points that the mean square distance between points is below a threshold square distance and the combination would provide for achieving acceptable range accuracy and detection sensitivity (Crouch, see at least [0004]).
In regard to claim 5
, Silver, as modified by Non-patent Literature Hata, teaches the method of claim 1, accordingly the rejection of claim 1 is incorporated.
Silver, as modified by Non-patent Literature Hata, is silent on all limitations of the claim.
However, Crouch teaches the additional points included in a given cluster are determined using a k-dimensional tree constructed based on features of each point of the point-cloud frame (Crouch, in at least [0059 & 0012], discloses a k-d tree (short for k-dimensional tree) is a space-partitioning data structure for organizing points in a k-dimensional space. A k-d tree and NN (nearest neighbor) search is performed in order to assign class membership to any unknown object with a 3D point cloud [i.e., the additional points included in a given cluster are determined using a k-dimensional tree constructed based on features of each point of the point-cloud frame]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify Silver, as already modified by Non-patent Literature Hata, in view of Crouch with a reasonable expectation of success, as both inventions are directed to the same field of endeavor – classifying an object in a point cloud – and use k-dimensional tree for assigning class membership and the combination would provide for achieving acceptable range accuracy and detection sensitivity (Crouch, see at least [0004]).
In regard to claim 10
, Silver, as modified by Non-patent Literature Hata, discloses the apparatus of claim 9,
Claim 10 recites an apparatus having substantially the same features of claim 2 above, therefore claim 10 is rejected for the same reasons as claim 2.
In regard to claim 11
, Silver, as modified by Non-patent Literature Hata, teaches the apparatus of claim 9, accordingly the rejection of claim 9 is incorporated.
Silver, as modified by Non-patent Literature Hata, is silent on all limitations of the claim.
However, Crouch teaches wherein a given cluster includes the additional points based on the additional points having feature values that are within a tolerance of corresponding feature values of the given cluster, wherein the feature values include a curvature value and a normal value (Crouch, in at least Figs. 6A-6B, 7, and [00680097], teaches in step 717, the closest fit is performed between a test input point cloud and the model point clouds associated with the first and second classes, in order to determine whether or not the object should be classified in the first or second class. If the closest fit is too large, e.g., the mean square distance between points in the test input point cloud and points in the model point cloud for a minimum ratio of closest points between the point clouds is above a threshold square distance [i.e., wherein a given cluster includes the additional points based on the additional points having feature values that are within a tolerance of corresponding feature values of the given cluster], then the object is considered not to belong to the class. If the mean square distance between points in the test input point cloud and points in the model point cloud for the top 90% of closest points between the point clouds is above 2 cm.sup.2, the object is considered not to belong to the class. Surface normals 602 [i.e., wherein the feature values include a curvature value and a normal value] are depicted in Fig. 6A and approximate a normal to the surface of the object at each point of the point cloud. Examiner notes, as depicted by Fig. 6A the surface normal are perpendicular to the curvature values. As such, the point clouds include a curvature value and a normal value).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify Silver, as already modified by Non-patent Literature Hata, in view of Crouch with a reasonable expectation of success, as both inventions are directed to the same field of endeavor – classifying an object in a point cloud – and include the points that the mean square distance between points is below a threshold square distance and include the curvature value and a normal value in the point clouds and the combination would provide for achieving acceptable range accuracy and detection sensitivity (Crouch, see at least [0004]).
In regard to claim 13
, Silver, as modified by Non-patent Literature Hata, teaches the apparatus of claim 9, accordingly the rejection of claim 9 is incorporated.
Silver, as modified by Non-patent Literature Hata, is silent on all limitations of the claim.
However, Crouch teaches wherein construct a k-dimensional tree from the point-cloud frame (Crouch, in at least [0059], teaches a k-d tree (short for k-dimensional tree) is a space-partitioning data structure for organizing points in a k-dimensional space [i.e., wherein construct a k-dimensional tree from the point-cloud frame]), and
include the additional points in a given cluster based on a nearest neighbor search that uses the k-dimensional tree (Crouch, in at least Figs. 5A-5B, [0059 & 0062], teaches K-d trees are a useful data structure for several applications, such as searches involving a multidimensional search key (e.g. range searches and nearest neighbor searches). The k-d tree 550 is used to perform a nearest neighbor (NN) search, which aims to find the point in the set that is nearest to a given input point [i.e., include the additional points in a given cluster based on a nearest neighbor search that uses the k-dimensional tree]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify Silver, as already modified by Non-patent Literature Hata, in view of Crouch with a reasonable expectation of success, as both inventions are directed to the same field of endeavor – classifying an object in a point cloud – and include the neighbor points in the same segment and the combination would provide for achieving acceptable range accuracy and detection sensitivity (Crouch, see at least [0004]).
In regard to claim 18
, Silver, as modified by Non-patent Literature Hata, teaches the non-transitory computer-readable medium of claim 17.
Claim 18 recites a non-transitory computer readable medium having substantially the same features of claim 2 above, therefore claim 18 is rejected for the same reasons as claim 2.
38. Claim(s) 4, and 12
is/are rejected under 35 U.S.C. 103 as being unpatentable over Silver et al. (US-9285230-B1) in view of Non-patent Literature Hata et al. (“Robust Curb Detection and Vehicle Localization in Urban Environments”) and further in view of Liu et al. (CN-107123164-B).
In regard to claim 4
, Silver, as modified by Non-patent Literature Hata, teaches the method of claim 1, wherein identifying the cluster includes:
identifying the cluster from remaining clusters of the plurality of clusters (Silver, in at least Col 14, lines 50-54, discloses the computing system processes received point clouds into a dense point cloud representation by accumulating multiple point clouds and processing the point clouds into the representation [i.e., identifying the cluster from remaining clusters of the plurality of clusters]).
Silver, as modified by Non-patent Literature Hata, is silent on removing certain clusters of the plurality of clusters based on a growth of a certain clusters stopping during a region-growing process in which the additional points are added to each cluster, and
However, Liu teaches removing certain clusters of the plurality of clusters based on a growth of a certain clusters stopping during a region-growing process in which the additional points are added to each cluster (Liu, in at least [0076 & 0082] teaches the point cloud preprocessing module uses a smoothing and denoising method to remove noise from different point clouds, and performs clustering using an improved region growing method. Outliers in the point cloud are removed based on the clustering results. The point cloud preprocessing module corresponds to steps (1) and (2) in the three-dimensional reconstruction method. By removing outliers [i.e., removing certain clusters of the plurality of clusters] from the point cloud during the preprocessing stage using an improved region growing [i.e., based on a growth of a certain clusters stopping during a region-growing process in which the additional points are added to each cluster] method, not only can point cloud data that better matches the true shape of the original object be generated, but the impact of noise generated by outliers during subsequent point cloud registration can also be reduced).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify Silver, as already modified by Non-patent Literature Hata, in view of Liu with a reasonable expectation of success, as both inventions are directed to the same field of endeavor – classifying an object in a point cloud – and remove noise from different point clouds and perform clustering and the combination would provide for a three-dimensional reconstruction method and system that preserves sharp features (Liu, see at least [0002]).
In regard to claim 12
, Silver, as modified by Non-patent Literature Hata, teaches the apparatus of claim 9.
Claim 12 recites an apparatus having substantially the same features of claim 4 above, therefore claim 12 is rejected for the same reasons as claim 4.
39. Claim(s) 8, and 16
is/are rejected under 35 U.S.C. 103 as being unpatentable over Silver et al. (US-9285230-B1) in view of Non-patent Literature Hata et al. (“Robust Curb Detection and Vehicle Localization in Urban Environments”) and further in view of Sakai et al. (US-20180341019-A1).
In regard to claim 8
, Silver, as modified by Non-patent Literature Hata, teaches the method of claim 1.
Silver, as modified by Non-patent Literature Hata, is silent on all limitations of the claim.
However, Sakai teaches wherein obtaining the point-cloud frame comprises:
acquiring two consecutive single-frame point-clouds (Sakai, in at least Fig. 1, and [0061], teaches the point clustering module 127 compares the space, angle, respective distance, etc. between adjacent points [i.e., acquiring two consecutive single-frame point-clouds] in the point cloud to a threshold space, angle, distance, etc.); and
determine an accumulated point-cloud by registering the two consecutive single-frame point clouds into a common coordinate system, wherein the point-cloud frame includes the accumulated point-cloud (Sakai, in at least in at least Fig. 1, and [0061], teaches the point clustering module 127 compares the space, angle, respective distance, etc. between adjacent points [i.e., two consecutive single-frame point clouds] in the point cloud to a threshold space, angle, distance, etc. When the space, angle, distance, etc. exceeds (or is equal to) the threshold, the point clustering module 127 determines that the point(s) beyond the cluster 500b should be grouped [i.e., an accumulated point-cloud] with a separate cluster. Examiner notes, when the space, angle, respective distance, etc. between adjacent points is less than the threshold, then the adjacent points are clustered together which is determine an accumulated point-cloud by registering the two consecutive single-frame point clouds into a common coordinate system, wherein the point-cloud frame includes the accumulated point-cloud).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify Silver, as already modified by Non-patent Literature Hata, in view of Sakai with a reasonable expectation of success, as both inventions are directed to the same field of endeavor – classifying an object in a point cloud – and cluster the adjacent point clouds when the space, angle, respective distance, etc. between the adjacent points is less than a threshold and the combination would provide for forming 3D point clouds representing the vehicle's environment (Sakai, see at least [0002]).
In regard to claim 16
, Silver, as modified by Non-patent Literature Hata, teaches the apparatus of claim 9.
Claim 16 recites an apparatus having substantially the same features of claim 8 above, therefore claim 16 is rejected for the same reasons as claim 8.
40. Claim(s) 21
is/are rejected under 35 U.S.C. 103 as being unpatentable over Silver et al. (US-9285230-B1) in view of Non-patent Literature Hata et al. (“Robust Curb Detection and Vehicle Localization in Urban Environments”) and further in view of Ekin (US-20080199045-A1).
In regard to claim 21
, Silver, as modified by Non-patent Literature Hata, teaches the method of claim 1, wherein:
detecting the road feature comprising detecting the curb of the road a(Silver, in at least Fig. 3, and Col 21, lines 44-47, discloses at block 310, the method 300 further includes, based on an output of the classification system, determining whether the given data points represent one or more road curbs [i.e., detecting a road feature from the cluster] in the environment of the vehicle);
Silver, as modified by Non-patent Literature Hata, is silent on wherein:
identifying a cluster from the plurality of clusters comprising identifying a largest cluster from the plurality of clusters; and
according to the boundary of the largest cluster.
However, Ekin teaches wherein:
identifying a cluster from the plurality of clusters comprising identifying a largest cluster from the plurality of clusters (Ekin, in at least Fig. 9, and [0019], teaches the selection of the road candidate pixels comprises identifying connected clusters of equal candidate pixel values in the mask, and selecting the largest coherent cluster [i.e., identifying a largest cluster from the plurality of clusters], that is larger than a predetermined percentage of all pixels, and has a lower boundary located below a certain row of the mask); and
according to the boundary of the largest cluster (Ekin, in at least [0021-0023], teaches the selection of a final road cluster comprises: correcting the initial road cluster, if the upper boundary of the initial road cluster is above a preselected second threshold by redefining the upper boundary of the initial road cluster with the initial horizon estimate; and selecting the initial road cluster, and adjusting the horizon estimate to be coinciding with the upper boundary of the initial road cluster [i.e., according to the boundary of the largest cluster], if the upper boundary of the initial road cluster is located below or at a predetermined second threshold).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify Silver, as already modified by Non-patent Literature Hata, in view of Ekin with a reasonable expectation of success, as all inventions are directed to the same field of endeavor – identifying and localizing three-dimensional (3D) objects – and identify and select the largest coherent cluster and use the cluster for detecting the curb of the road based on the boundary of the initial road cluster and the combination would provide for robust detection of objects on a road so as to warn a driver or to enhance a view available to the driver and robust detection of objects on a road so as to warn a driver or to enhance a view available to the driver (Ekin, see at least [0003 & 0007]).
41. Claim(s) 22
is/are rejected under 35 U.S.C. 103 as being unpatentable over Silver et al. (US-9285230-B1) in view of Non-patent Literature Hata et al. (“Robust Curb Detection and Vehicle Localization in Urban Environments”) and further in view of Kee et al. (US-20180161986-A1).
In regard to claim 22
, Silver, as modified by Non-patent Literature Hata, teaches the method of claim 1, accordingly the rejection of claim 1 is incorporated.
Further, Non-patent Literature Hata teaches wherein the method further comprises: smoothing the boundary of a largest cluster based on a spline function (Non-patent Literature Hata, in at least p. 1257, teaches independently of the sensor adopted in the curb detection task, they are all susceptible to occlusion when considered a typical urban environment. The most common approach to deal with occlusions is by fitting a model (e.g. spline) in the curb model [i.e., smoothing the boundary of a largest cluster based on a spline function] and remove points that not match with the model);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify Silver, as modified by Non-patent Literature Hata, further in view of Non-patent Literature Hata with a reasonable expectation of success, as both inventions are directed to the same field of endeavor – curb detection – and use a spline fitting model for smoothing the boundaries of the clusters, including the largest cluster, and the combination would provide for matching the detected curb information against it to obtain accurate pose estimation, which is a fundamental capability to self-driving cars (Non-patent Literature Hata, see at least p. 1257).
Silver, as modified by Non-patent Literature Hata, is silent on wherein detecting the curb of the road is further based on a concave hull of the largest cluster.
However, Kee teaches wherein detecting the curb of the road is further based on a concave hull of the largest cluster (Kee, in at least [0050], teaches once the factor graph is optimized, STORM projects the object models and background point clouds into a global coordinate frame. Point clouds of the background scene excludes object points to avoid aliasing with the object point clouds. These background point clouds are generated from the original sensor point clouds at each sensor pose. This is done by first computing the concave hull of the objects' database point clouds in the sensor frame. Then, all points inside the hull are removed from the background cloud [i.e., detecting the curb of the road is further based on a concave hull of the largest cluster, especially when the largest cluster is used]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify Silver, as already modified by Non-patent Literature Hata, in view of Kee with a reasonable expectation of success, as both inventions are directed to the same field of endeavor – identifying and localizing three-dimensional (3D) objects – and use concave hull to detect a three-dimensional object, such as curbs of a road and the combination would provide for improving localization and mapping (Kee, see at least [0004]).
Conclusion
42. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Mei et al. (US-20190163990-A1) teaches a system and method for large-scale lane marking detection using multimodal sensor data.
Poelman et al. (US-20170046589-A1) teaches pre-segmenting point cloud data to run real-time shape extraction.
43. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
44. 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.
45. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Preston J Miller whose telephone number is (703)756-1582. The examiner can normally be reached Monday through Friday 7:30 AM - 4:30 PM EST.
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47. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ramya P Burgess can be reached at (571) 272-6011. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/P.J.M./Examiner, Art Unit 3661
/Tarek Elarabi/Primary Examiner, Art Unit 3661
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