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 Rejections - 35 USC § 101
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.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
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)
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
Claim 1 is directed to a method of controlling a vehicle (i.e., a process). Therefore, claim 1 is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis, 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)
Independent claim 1 includes limitations that recite an abstract idea (emphasized below [with the category of abstract idea in brackets]) and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites:
A computer-implemented method, comprising:
obtaining sensor data descriptive of an environment of an autonomous vehicle;
obtaining a plurality of travel way markers from map data descriptive of the environment;
determining, using a machine-learned object detection model and based on the sensor data, an association between one or more travel way markers of the plurality of travel way markers and an object in the environment [mental process/step]; and
generating, using the machine-learned object detection model, an offset with respect to the one or more travel way markers of a spatial region of the environment associated with the object [mental process/step].
The examiner submits that the foregoing bolded limitations constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “determining…” in the context of this claim encompasses a person (driver) looking at data collected and forming a simple judgement. Accordingly, the claim recites at least one abstract idea. Similar analyses apply to independent claims 17 and 20.
101 Analysis – Step 2A, Prong II
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.”
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”.):
A computer-implemented method, comprising:
obtaining sensor data descriptive of an environment of an autonomous vehicle [pre-solution activity (data gathering) using generic sensors];
obtaining a plurality of travel way markers from map data descriptive of the environment [pre-solution activity (data gathering)];
determining, using a machine-learned object detection model and based on the sensor data, an association between one or more travel way markers of the plurality of travel way markers and an object in the environment [mental process/step]; and
generating, using the machine-learned object detection model, an offset with respect to the one or more travel way markers of a spatial region of the environment associated with the object [mental process/step].
For the following reasons, the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of “obtaining sensor data…,” and “obtaining a plurality of travel way markers…,”, the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer (vehicle controller) to perform the process. In particular, the receiving steps from the sensors and from the external source are recited at a high level of generality (i.e. as a general means of gathering vehicle and road condition data for use in the evaluating step), and amounts to mere data gathering, which is a form of insignificant extra-solution activity.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitations as an ordered combination or as a whole, the limitations 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
Regarding Step 2B of the Revised Guidance, representative independent claim 1 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 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. And as discussed above, the additional limitations of “obtaining sensor data …,” and “obtaining a plurality of travel way markers…,” the examiner submits that these limitations are insignificant extra-solution activities. In addition, these additional limitations (and the combination, thereof) amount to no more than what is well-understood, routine and conventional activity. Hence, the claim is not patent eligible.
Dependent claims 2-16, 18 and 19 do not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. The dependent claims comparisons of measured velocity values with acceptable thresholds, and calculation of data offset values. Therefore, dependent claims 2-16, 18 and 19 are not patent eligible under the same rationale as provided for in the rejection of claim 1.
Therefore, claims 1-20 are ineligible under 35 USC §101.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Goldman, et al., US 2022/0228882 A1.
As per Claim 1, Robinson, et al., US 2023/0054759 A1 teaches a computer-implemented method (¶¶ 19-20), comprising:
obtaining sensor data descriptive of an environment of an autonomous vehicle (¶ 26; sensors aboard 500 of Figure 5);
obtaining a plurality of travel way markers from map data descriptive of the environment (¶¶ 77, 84); and
determining, using a machine-learned object detection model and based on the sensor data, an association between one or more travel way markers of the plurality of travel way markers and an object in the environment (¶¶ 76-77).
Robinson does not expressly teach generating, using the machine-learned object detection model, an offset with respect to the one or more travel way markers of a spatial region of the environment associated with the object. Goldman teaches generating, using the machine-learned object detection model, an offset with respect to the one or more travel way markers of a spatial region of the environment associated with the object (¶¶ 177-178). At the time of the invention, a person of skill in the art would have thought it obvious to combine the sensor system of Robinson with the mapping system of Goldman, in order to process map change updates more quickly.
As per Claim 2, Robinson teaches that the travel way markers include lane markers (¶ 152).
As per Claim 3, Robinson teaches that the lane markers comprise centerline markers (¶ 147; for “lane centering (LC)” purposes).
As per Claim 4, Robinson teaches:
inputting the travel way markers and the sensor data to the machine-learned object detection model (¶ 166; through “anomaly detection”); and
obtaining object data from the machine-learned object detection model at projected locations of the travel way markers in a reference frame of the sensor data, wherein the object data indicates that the object is likely to be present at a projected location of the one or more travel way markers (¶¶ 167-168; through “computer vision and/or other machine learning object classification techniques”).
As per Claim 5, Robinson teaches that obtaining the object data comprises subsampling, based on the travel way markers, a detection map generated by the machine-learned object detection model (¶¶ 133, 165).
As per Claim 6, Robinson teaches that one or more portions of the machine-learned object detection model are configured to sparsely use portions of an output layer based on locations in the sensor data corresponding to the projected locations (¶¶ 108-109; in connection with “3D location estimates of the object obtained from the neural network”).
As per Claim 7, Robinson does not expressly teach that the machine-learned object detection model is trained by: obtaining ground truth travel way marker labels indicating a ground truth association between the object and one or more of the travel way markers; and determining, based on comparing the object data and the ground truth travel way marker labels, a sparse loss for the machine-learned object detection model. Goldman teaches that the machine-learned object detection model is trained by:
obtaining ground truth travel way marker labels indicating a ground truth association between the object and one or more of the travel way markers (¶ 180; by measuring “a heading error”); and
determining, based on comparing the object data and the ground truth travel way marker labels, a sparse loss for the machine-learned object detection model (¶¶ 197-198; based on “three-dimensional polynomial representations of preferred vehicle paths along a road”).
See Claim 1 above for the rationale based on obviousness, motivations and reasons to combine.
As per Claim 8, Robinson teaches:
determining an offset of a centroid of a boundary of the spatial region (¶¶ 50-51); and
determining one or more dimensions of the boundary (¶ 50).
As per Claim 9, Robinson teaches:
determining a first offset of a centroid of a first boundary of the spatial region in two dimensions (¶ 50); and
determining a second offset of a centroid of a second boundary of the spatial region in three dimensions (¶¶ 50-51).
As per Claim 10, Robinson teaches:
based on determining that a velocity of the object is below a threshold, outputting a characteristic for the object indicating that the object is a static object (¶ 135; “distinguishing between static and moving objects”); and
outputting the characteristic to a motion planning system of the autonomous vehicle (¶ 139; to aid in “emergency braking, collision avoidance, and/or other functions”).
As per Claim 11, Robinson teaches:
based on determining that a velocity of the object is below a threshold (¶ 135; “distinguishing between static and moving objects”) and that the object is located adjacent to a travel way in the environment, outputting a characteristic for the object indicating that the object is a static object (¶ 135; among “vehicles entering or leaving the vehicle's 500 lane” as in Figure 5A); and
outputting the characteristic to a motion planning system of the autonomous vehicle (¶ 139; to aid in “emergency braking, collision avoidance, and/or other functions”).
As per Claim 12, Robinson teaches: sampling discrete travel way markers from continuous travel way map data (¶¶ 165-166; “to train machine learning models”).
As per Claim 13, Robinson teaches that the spatial region of the environment is beyond an effective range of a LIDAR sensor of the autonomous vehicle (¶¶ 140-141; given short-range and mid-range lidar sensors).
As per Claim 14, Robinson teaches:
that the machine-learned object detection model was trained using training sensor data having a training field of view and training travel way markers having a training resolution (¶ 127);
that the sensor data is associated with a runtime field of view (¶ 135); and
the travel way markers are obtained in (c) at a runtime resolution selected based on a comparison of the training field of view and the runtime field of view (¶ 141; “to illuminate vehicle surroundings up to approximately 200 m”).
As per Claim 15, Robinson teaches:
projecting, using a projection transform, the travel way markers into a reference frame of the sensor data (¶ 36);
determining one or more offsets of the spatial region with respect to the travel way markers (¶ 49);
based on the determined one or more offsets, determining a projection error for the projected travel way markers; and recalibrating the projection transform based on the determined projection error (¶102; through “error correcting code (ECC) memory”).
As per Claim 16, Robinson teaches identifying a lane in which the object is located based on the one or more travel way markers (¶ 107; “lane detection” through “computer stereo vision”).
As per Claim 17, Robinson teaches an autonomous vehicle control system for controlling an autonomous vehicle (¶ 44; vehicle controller 116 of Figure 1A), the autonomous vehicle control system comprising:
one or more processors (¶ 46); and
one or more non-transitory computer-readable media (¶ 46) storing instructions that are executable by the one or more processors to cause the autonomous vehicle control system to perform operations, the operations comprising:
obtaining sensor data descriptive of an environment of the autonomous vehicle (¶ 54; through “LIDAR sensors”);
obtaining a plurality of travel way markers from map data descriptive of the environment (¶¶ 77, 84); and
determining, using a machine-learned object detection model and based on the sensor data, an association between one or more travel way markers of the plurality of travel way markers and an object in the environment (¶¶ 76-77).
Robinson does not expressly teach generating, using the machine-learned object detection model, an offset with respect to the one or more travel way markers of a spatial region of the environment associated with the object. Goldman teaches generating, using the machine-learned object detection model, an offset with respect to the one or more travel way markers of a spatial region of the environment associated with the object (¶¶ 177-178). See Claim 1 above for the rationale based on obviousness, motivations and reasons to combine.
As per Claim 18, Robinson teaches:
inputting the travel way markers and the sensor data to the machine-learned object detection model (¶ 166; through “anomaly detection”); and
obtaining object data from the machine-learned object detection model at projected locations of the travel way markers in a reference frame of the sensor data, wherein the object data indicates that the object is likely to be present at a projected location of the one or more travel way markers (¶¶ 167-168; through “computer vision and/or other machine learning object classification techniques”).
As per Claim 19, Robinson teaches:
inputting the travel way markers and the sensor data to the machine-learned object detection model (¶ 166; through “anomaly detection”); and
obtaining object data from the machine-learned object detection model at projected locations of the travel way markers in a reference frame of the sensor data, wherein the object data indicates that the object is likely to be present at a projected location of the one or more travel way markers (¶¶ 167-168; through “computer vision and/or other machine learning object classification techniques”);
wherein obtaining the object data comprises subsampling, based on the travel way markers, a detection map generated by the machine-learned object detection model (¶¶ 133, 165).
As per Claim 20, Robinson teaches one or more non-transitory computer-readable media (¶ 46) storing instructions that are executable by one or more processors to cause an autonomous vehicle control system to perform operations, the operations comprising:
obtaining sensor data descriptive of an environment of an autonomous vehicle (¶ 54; through “LIDAR sensors”);
obtaining a plurality of travel way markers from map data descriptive of the environment (¶¶ 77, 84); and
determining, using a machine-learned object detection model and based on the sensor data, an association between one or more travel way markers of the plurality travel way markers and an object in the environment (¶¶ 76-77).
Robinson does not expressly teach generating, using the machine-learned object detection model, an offset with respect to the one or more travel way markers of a spatial region of the environment associated with the object. Goldman teaches generating, using the machine-learned object detection model, an offset with respect to the one or more travel way markers of a spatial region of the environment associated with the object (¶¶ 177-178). See Claim 1 above for the rationale based on obviousness, motivations and reasons to combine.
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.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-21 of U.S. Patent No. 12,223,677 (“the ‘677 patent”). Although the claims at issue are not identical, they are not patentably distinct from each other because claim 1 of the pending application includes a step of “obtaining sensor data descriptive of an environment of an autonomous vehicle,” as does claim 1 of the ‘677 patent.
Claim 1 of the pending application also includes a step of “obtaining a plurality of travel way markers from map data descriptive of the environment,” as does claim 1 of the ‘677 patent.
Claim 1 of the pending application also includes a step of “determining, using a machine-learned object detection model and based on the sensor data, an association between one or more travel way markers of the plurality of travel way markers and an object in the environment,” as does claim 1 of the ‘677 patent.
Claim 1 of the pending application also includes a step of “generating, using the machine-learned object detection model, an offset with respect to the one or more travel way markers of a spatial region of the environment associated with the object,” as does claim 1 of the ‘677 patent.
Conclusion
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ATUL TRIVEDI
Primary Examiner
Art Unit 3661
/ATUL TRIVEDI/Primary Examiner, Art Unit 3661
Prosecution Insights