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 .
Priority
This application discloses and claims only subject matter disclosed in prior application, and names the inventor or at least one joint inventor named in the prior application. Accordingly, this application may constitute a continuation or divisional. Should applicant desire to claim the benefit of the filing date of the prior application, attention is directed to 35 U.S.C. 120, 37 CFR 1.78, and MPEP § 211 et seq. The presentation of a benefit claim may result in an additional fee under 37 CFR 1.17(w)(1) or (2) being required, if the earliest filing date for which benefit is claimed under 35 U.S.C. 120, 121, 365(c), or 386(c) and 1.78(d) in the application is more than six years before the actual filing date of the application.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/28/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Instant Application (US19/285394)
US12412278
Claim 1: An autonomous or semi-autonomous machine comprising: one or more central processing units (CPUs);one or more graphics processing units (GPUs);one or more hardware accelerators; and one or more sensors having one or more fields of view or one or more sensory fields external to the autonomous or semi-autonomous machine, wherein the autonomous or semi-autonomous machine is to: evaluate a target space in an environment based at least on an indication of one or more adjustments to a shape corresponding to the target space, the indication determined using one or more machine learning models (MLMs) and sensor data obtained using the one or more sensors; and perform one or more planning, parking, control, or navigation operations based at least on the evaluation.
Claim 11: A system comprising: one or more processors to execute operations comprising: applying, to one or more machine learning models (MLMs) trained to predict likelihoods that one or more points of a multi-dimensional shape correspond to one or more portions of a boundary of a target space, the boundary representing an entrance to the target space, sensor data corresponding to at least one field of view or sensory field of at least one sensor, the at least one field of view or sensory field including at least a portion of the target space; receiving, from the one or more MLMs, data indicating the one or more points correspond to the one or more portions of the boundary of the target space; identifying, using the data, one or more locations associated with the one or more points as corresponding to boundary of the target space; and performing one or more planning, control, parking, or navigation operations corresponding to a machine based at least on the identifying of the one or more locations as corresponding to the boundary.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No.12412278. Although the claims at issue are not identical, they are not patentably distinct from each other because it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the boundary representing an entrance to the target space, sensor data corresponding to at least one field of view or sensory field of at least one sensor as an overall target space for a parking algorithm.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Micks et al. (US20180025640) (hereinafter Micks) in view of Lei et al. (US20180128638) (hereinafter Lei).
Regarding claim 1, Micks discloses an autonomous or semi-autonomous machine comprising: one or more central processing units (CPUs); one or more graphics processing units (GPUs); one or more hardware accelerators [0021-0028; computing architecture for self-autonomous vehicle parking].
and one or more sensors having one or more fields of view or one or more sensory fields external to the autonomous or semi-autonomous machine [0040-0044; sensors used in identifying parking area and availability].
perform one or more planning, parking, control, or navigation operations based at least on the evaluation [Figs. 1-6, 0038-0047, 0053-0062, 0072-0092; confirming parking spot availability for planned parking algorithm].
Micks discloses the limitations of the claim. However, Micks does not explicitly disclose wherein the autonomous or semi-autonomous machine is to: evaluate a target space in an environment based at least on an indication of one or more adjustments to a shape corresponding to the target space, the indication determined using one or more machine learning models (MLMs) and sensor data obtained using the one or more sensors.
Lei more explicitly discloses wherein the autonomous or semi-autonomous machine is to: evaluate a target space in an environment based at least on an indication of one or more adjustments to a shape corresponding to the target space, the indication determined using one or more machine learning models (MLMs) and sensor data obtained using the one or more sensors [0008-0010, 0040-0048, 0063-0084, 0125, 0166-0174; planning and drawing navigation route on distribution map for planned parking].
It would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Micks with the teachings of Lei as stated above. By incorporating the teachings as such, increased efficiency and accuracy across different parking situations is achieved (see Lei 0005-0008).
Regarding claim 2, Micks discloses wherein the indication defines at least one of: one or more displacement values to one or more portions of the shape; one or more adjusted points relative to the shape; one or more adjusted boundaries of the shape; or one or more skewed versions of the shape [Figs. 1-6, 0038-0047, 0053-0062, 0072-0092; splines and edges of parking spot and optimal path].
Regarding claim 3, Micks discloses wherein the evaluation of the target space is based at least on the autonomous or semi-autonomous machine applying at least one of the one or more adjustments to one or more portions corresponding to the shape [Figs. 1-6, 0038-0047, 0053-0062, 0072-0092; splines and edges of parking spot and optimal path].
Regarding claim 4, Micks discloses wherein the one or more MLMs are to generate the indication based at least on processing input data corresponding to the sensor data [Figs. 1-6, 0040-0044; sensors used in identifying parking area and availability].
Regarding claim 5, Micks discloses the limitations of the claim. However, Micks does not explicitly disclose wherein the indication corresponds to a shape selected from a plurality of shapes associated with a spatial region within a representation of the one or more fields of view or one or more sensory fields.
Lei discloses wherein the indication corresponds to a shape selected from a plurality of shapes associated with a spatial region within a representation of the one or more fields of view or one or more sensory fields [0063-0066; parking space navigation route with a variety of representations on the map].
It would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Micks with the teachings of Lei for the same reasons as stated above.
Regarding claim 6, Micks discloses wherein the sensor data comprises image data representative of one or more images, and the one or more adjustments are relative to coordinates defining the shape in the one or more images [0072-0095; parking space navigation performing updated radar detections and continually updating spines].
Regarding claim 7, Micks discloses wherein the evaluation is based at least on one or more confidence values indicating a likelihood that the shape corresponds to the target space [0072-0095; parking space navigation performing updated radar detections and continually updating spines].
Regarding claim 8, Micks discloses A system comprising: one or more central processing units (CPUs); one or more graphics processing units (GPUs);one or more hardware accelerators [0021-0028; computing architecture for self-autonomous vehicle parking].
and one or more sensors having one or more fields of view or one or more sensory fields [0040-0044; sensors used in identifying parking area and availability].
wherein the system causes a machine to perform one or more planning, parking control, or navigation operations based at least on a geometry of a target space in an environment [Figs. 1-6, 0038-0047, 0053-0062, 0072-0092; confirming parking spot availability for planned parking algorithm].
sensor data obtained using the one or more sensors [0040-0044, 0072-0092; sensors used in identifying parking area and availability].
Micks discloses the limitations of the claim. However, Micks does not explicitly disclose the geometry of the target space identified based at least on one or more adjustments to one or more shapes corresponding to the target space as determined using one or more machine learning models (MLMs).
Lei more explicitly discloses the geometry of the target space identified based at least on one or more adjustments to one or more shapes corresponding to the target space as determined using one or more machine learning models (MLMs) [0008-0010, 0040-0048, 0063-0084, 0125, 0166-0174; planning and drawing navigation route on distribution map for planned parking].
It would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Micks with the teachings of Lei as stated above. By incorporating the teachings as such, increased efficiency and accuracy across different parking situations is achieved (see Lei 0005-0008).
Regarding claim 9, Micks discloses wherein the one or more adjustments correspond to at least one of: one or more displacement values to one or more portions of the one or more shapes; one or more adjusted points relative to the one or more shapes; one or more adjusted boundaries of the one or more shapes; or one or more skewed versions of the one or more shapes [Figs. 1-6, 0038-0047, 0053-0062, 0072-0092; splines and edges of parking spot and optimal path].
Regarding claim 10, Micks discloses wherein the geometry is identified is based at least on the system applying at least one of the one or more adjustments to one or more portions corresponding to the one or more shapes [Figs. 1-6, 0038-0047, 0053-0062, 0072-0092; splines and edges of parking spot and optimal path].
Regarding claim 11, Micks discloses wherein the one or more MLMs are to generate the one or more adjustments based at least on processing input data corresponding to the sensor data [0072-0095; parking space navigation performing updated radar detections and continually updating spines].
Regarding claim 12, Micks discloses the limitations of the claim. However, Micks does not explicitly disclose wherein the one or more adjustments correspond to a shape selected from a plurality of shapes associated with a spatial region within a representation of the one or more fields of view or one or more sensory fields.
Lei discloses wherein the one or more adjustments correspond to a shape selected from a plurality of shapes associated with a spatial region within a representation of the one or more fields of view or one or more sensory fields [0063-0066; parking space navigation route with a variety of representations on the map].
It would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Micks with the teachings of Lei for the same reasons as stated above.
Regarding claim 13, Micks discloses wherein the sensor data comprises image data representative of one or more images, and the one or more adjustments are relative to coordinates defining the one or more shapes in the one or more images [Figs. 1-6, 0038-0047, 0053-0062, 0072-0092; splines and edges of parking spot and optimal path].
Regarding claim 14, Micks discloses wherein the geometry is identified based at least on one or more confidence values indicating a likelihood that the one or more shapes correspond to the target space [0072-0095; parking space navigation performing updated radar detections and continually updating spines].
Regarding claim 15, Micks discloses wherein the system is comprised in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations; a system for performing deep learning operations; a system on chip (SoC);a system including a programmable vision accelerator (PVA);a system implemented using a robot; a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources [Figs. 1-6, 0024-0032; learning model and device for vehicle self-autonomous parking].
Regarding claim 16, Micks discloses at least one system-on-a-chip (SoC) comprising: one or more central processing units (CPUs);one or more graphics processing units (GPUs); and one or more hardware accelerators [0021-0028; computing architecture for self-autonomous vehicle parking].
wherein the at least one SoC causes a machine to perform one or more planning, parking, control, or navigation operations with respect to a target space in an environment [Figs. 1-6, 0038-0047, 0053-0062, 0072-0092; confirming parking spot availability for planned parking algorithm].
sensor data obtained using one or more sensors of the machine [0040-0044, 0072-0092; sensors used in identifying parking area and availability].
Micks discloses the limitations of the claim. However, Micks does not explicitly disclose the target space determined based at least on one or more adjustments to one or more shapes corresponding to the target space as determined using one or more machine learning models (MLMs).
Lei more explicitly discloses the target space determined based at least on one or more adjustments to one or more shapes corresponding to the target space as determined using one or more machine learning models (MLMs) [0008-0010, 0040-0048, 0063-0084, 0125, 0166-0174; planning and drawing navigation route on distribution map for planned parking].
It would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Micks with the teachings of Lei as stated above. By incorporating the teachings as such, increased efficiency and accuracy across different parking situations is achieved (see Lei 0005-0008).
Regarding claim 17, Micks discloses wherein the one or more adjustments correspond to at least one of: one or more displacement values to one or more portions of the one or more shapes; one or more adjusted points relative to the one or more shapes; one or more adjusted boundaries of the one or more shapes; or one or more skewed versions of the one or more shapes [Figs. 1-6, 0038-0047, 0053-0062, 0072-0092; splines and edges of parking spot and optimal path].
Regarding claim 18, Micks discloses wherein the target space is determined based at least on the SoC applying at least one of the one or more adjustments to one or more portions corresponding to the one or more shapes [Figs. 1-6, 0038-0047, 0053-0062, 0072-0092; splines and edges of parking spot and optimal path].
Regarding claim 19, Micks discloses wherein the one or more MLMs are to generate the one or more adjustments based at least on processing input data corresponding to the sensor data [0072-0095; parking space navigation performing updated radar detections and continually updating spines].
Regarding claim 20, Micks discloses wherein the SoC is comprised in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations; a system for performing deep learning operations; a system including a programmable vision accelerator (PVA);a system implemented using a robot; a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources [Figs. 1-6, 0024-0032; learning model and device for vehicle self-autonomous parking].
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TALHA M NAWAZ whose telephone number is (571)270-5439. The examiner can normally be reached Flex, M-R 6:30am-3:30pm; F 8:30am-12:30pm.
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/TALHA M NAWAZ/Primary Examiner, Art Unit 2483
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