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 .
Claim Interpretation
1. The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: means for generating a height gradient map from the plurality of height maps; means for fusing the 3D sensor features and the height gradient map to generate height informed fused features; and means for performing the perception task using the height informed fused features.in claim 33.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
2. 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.
Claim33 is 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 .since the generic placeholder is not preceded by a structural modifier in specification for claim limitations such as : means for generating a height gradient map from the plurality of height maps; means for fusing the 3D sensor features and the height gradient map to generate height informed fused features; and means for performing the perception task using the height informed fused features.in claim 33.
Claim Rejections - 35 USC § 103
3. 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 1,12-14,17,28-30 and 33 are rejected under 35 U.S.C. 103 as being unpatentable over Liang et al. US 20200025935(hereinafter Liang) in view of Vaello Paños et al. US 20220004739(hereinafter Vaello).
Regarding claim 1, Liang provides for a memory; and processing circuitry connected to the memory ( Fig.1 see [0072], see “computing system 104 can include one or more processors and one or more memory devices”), the processing circuitry configured to:
generate 3D sensor features from data from one or more sensors (Fig.9, see BEV stream, see “[0140] In accordance with the disclosed technology, 3D object detection can be performed in bird's eye view (BEV). These detectors are effective as BEV maintains the structure native to 3D sensors such as LIDAR” ; generate a plurality of height maps from the 3D sensor features at a plurality of times ( Fig. 9 see bottom part, see maps);
generate a height gradient map from the plurality of height maps ( Fig. 9 see bottom part, see the map to the right before output to detection header) ; fuse the 3D sensor features and the height gradient map to generate height informed fused features ( Fig.9 see fusion layers, see continuous fusion from camera stream added to BEV stream); and perform the perception task using the height informed fused features ( see “[0105] Referring again to FIGS. 1 and 2, a computing system (e.g., vehicle computing system 112 of FIG. 1) can be configured to receive the detector output(s) 232 from object detection system 200 of FIG. 2. For example, detector output(s) 232 can be provided to one or more of the perception system124”.
Camera system of Liang does not provide for 3D features. Vaello teaches the above missing limitation of Liang, (see[0027] of Vaello. see “in combination with a 2D image of the environment obtained by the imaging sensor 104, are converted into a 3D image 122 of the environment 105 by one or more image processors 120”. It would have been obvious to one of the ordinary skill in the art before the effective filing date of the clamed invention, to combine the teaching of Vaello with the system and method of Liang, in order to obtain the claimed invention, by converting the 2D image of the environment obtained by the imaging sensor 104, into a 3D image 122 of the environment 105 by one or more image processors 120, a finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable (MPEP 2143).
Regarding claim 12, Liang provides for, wherein the perception task includes one or more of sematic segmentation, semantic occupancy prediction, lane tracking, or 3D object detection.(see [0095], see “When real-time location sensors within the autonomous vehicle determine a current geographic location of the autonomous vehicle, geographic prior data (e.g., geometric ground prior data and/or semantic road prior data) associated with that current geographic location can be retrieved from the map system 206 (e.g., from HD map database 216)”.
Regarding claim 13, Liang provides for, wherein the one or more sensors include one or more camera sensors. see [0081], see “the one or more autonomy system sensors 114 can include a Light Detection and Ranging (LIDAR) system, a Radio Detection and Ranging (RADAR) system, one or more cameras (e.g., visible spectrum cameras and/or infrared cameras), motion sensors, and/or other types of imaging capture devices and/or sensors. The autonomy sensor data.
Regarding claim 14, Liang provides for, wherein the one or more sensors include a camera sensor and a radar sensor (see [0081], see “the one or more autonomy system sensors 114 can include a Light Detection and Ranging (LIDAR) system, a Radio Detection and Ranging (RADAR) system, one or more cameras (e.g., visible spectrum cameras and/or infrared cameras), motion sensors, and/or other types of imaging capture devices and/or sensors. The autonomy sensor data”).
Regarding claims 28,29 and 30, see the rejections of claims 12,13 and 14 respectively, the recite similar limitations as claims 28-29 and 30. Hence they are similarly analyzed and rejected.
Regarding claims 17 and 33, see the rejection of claim 1. They recite similar limitations as claim 1. Hence, they are similarly analyzed and rejected.
Claims 15 and 31 are rejected under 35 U.S.C. 103 as being unpatentable over Liang et al. US 20200025935(hereinafter Liang) in view of Vaello Paños et al. US 20220004739(hereinafter Vaello), further in view of ANCORA et al. US 20240107169 (hereinafter Ancora).
Claim 15, Liang as modified by Vaello does not provide for, wherein the one or more sensors include a camera sensor and a sonar sensor. Ancora teaches the above missing limitation of Liang as modified by Vaello (see [0033] of Ancora , see “ Advantageously, the measurement sensor comprises a sonar, a lidar, a 2D or 3D radar, and/or a computerized module for estimating distance based on images, alone or in combination”. see “[0033] Advantageously, the measurement sensor comprises a sonar, a lidar, a 2D or 3D radar, and/or a computerized module for estimating distance based on images, alone or in combination. It would have been obvious to one of the ordinary skill in the art before the effective filing date of the clamed invention, to combine the teaching of Ancora with the system and method of Liang as modified by Vaello , in order to obtain the claimed invention, for estimating distance based on images, a finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable (MPEP 2143).
Regarding claim 31, see the rejection of claim 15. It recites similar limitation as claim 31. Hence it is similarly analyzed and rejected.
Claims 16 and 32 are rejected under 35 U.S.C. 103 as being unpatentable over Liang et al. US 20200025935(hereinafter Liang) in view of Vaello Paños et al. US 20220004739(hereinafter Vaello), further in view of Li et al. US 12515706 (hereinafter Li).
Claims 16 and 32 are rejected under 35 U.S.C. 103 as being unpatentable
Claim 16, Liang as modified by Vaello does not provide for, wherein the processing circuitry is part of an advanced driver assistance system (ADAS), and wherein the ADAS is configured to control a vehicle at least in part based on an output of the perception task. Li teaches the above missing limitation of Liang as modified by Vaello, see [0104] of Li
, see “In at least one embodiment, front-facing cameras may also be used for ADAS functions and systems including, without limitation, Lane Departure Warnings (“LDW”), Autonomous Cruise Control (“ACC”), and/or other functions such as traffic sign recognition”. It would have been obvious to one of the ordinary skill in the art before the effective filing date of the clamed invention, to combine the teaching of Li with the system and method of Liang as modified by Vaello , in order to obtain the claimed invention, via ADAS function and system including Lane Departure Warnings (“LDW”), Autonomous Cruise Control (“ACC”), and/or other functions such as traffic sign recognition. a finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable (MPEP 2143).
Regarding claim 32, see the rejection of claim 16. It recites similar limitation as claim 32. Hence it is similarly analyzed and rejected.
Allowable Subject Matter
4. Claims 2-11 and 18-27 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Reasons for Allowance
The following is an examiner’s statement of reasons for allowance: the prior arts of over Liang et al. US 20200025935 in view of Vaello Paños et al. US 20220004739, further in view of ANCORA et al. US 20240107169 and Li et al. US 12515706, failed to teach or suggest for features/limitations of claims 2-11 and 18-27.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bangalore Ravi et al. US 20250086978, is cited because the reference teaches “[0005] Continuous processing of image data and position data to generate BEV features may involve converting two-dimensional (2D) camera images into 3D representations of a 3D environment. Since point cloud frames already represent 3D representations of the 3D environment, converting 2D camera images into 3D representations of the 3D environment may allow the system to process both image data and position data in a continuous space”.
DWORAK et al. US 20250014354, is cited because the reference teaches “The CDSM techniques described above may be adapted to enable 3D image features to be presented alongside other types of 3D sensor features, which are aggregated on a shared BEV grid with respect to the vehicle”, in [0070].
Wu et al. US 12050660 , is cited because the reference teaches “Provided are methods for end-to-end perception system training using fused images, which can include fusing different types of images to form a fused image, extracting features from the fused image, calculating a loss, and modifying at least one network parameter of an image semantic network based on the loss. Systems and computer program products are also provided”, see abstract.
Contact Information
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/ALI BAYAT/Primary Examiner, Art Unit 2677
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