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 .
Summary of Claims
Claims 1-2, 5-10, and 13-18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Huval (US 2018/0373980).
Claims 3-4, 11-12, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Huval in view of Schuh (US 2020/0201356).
Priority Date
The priority date of this application is 02/01/2019.
Response to Arguments
The applicant has argued that Huval does not disclose “the machine learning model is trained based on a training dataset comprising a plurality of time series elements and a determined ground truth derived from at least a portion of the plurality of time series elements” and even more specifically has argued that Huval does not suggest “a determined ground truth derived from at least a portion of the plurality of time series elements”. The argument is that the human annotator disclosed in Huval is labeling individual images from which ground truth labels are derived and therefore Huval does not disclosed that the ground truth is derived from at least “a portion of the plurality of time series elements”. This argument is not persuasive, however, as a single image still constitutes a “portion” of said elements and as Huval discloses that the images are associated with a timestamp and further refers to the images as sequential frames (see Paragraphs [0026] and [0037]) the images clearly represent time series elements and therefore Huval discloses all of the elements of the claim.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 5-10, and 13-18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Huval (US 2018/0373980).
As per Claim 1:
Huval discloses the following limitations:
“one or more processors configured to: obtain sensor data associated with one or more sensors of a vehicle… and determine one or more control signals to control operation of the vehicle based on the three-dimensional feature; and provide data associated the one or more control signals to cause operation of the vehicle in accordance with the one or more control signals.”
Huval Paragraph [0018] discloses feeding sensor data into a neural network to identify three-dimensional objects in the environment of an autonomous vehicle that subsequentially generates control signals based on said identification.
“determine a three-dimensional feature associated with the sensor data based on a machine learning model, wherein the machine learning model is trained based on a training dataset comprising a plurality of time series elements and a determined ground truth derived from at least a portion of the plurality of time series elements, the machine learning model configured to generate an output representing the three-dimensional feature based on the sensor data”
Huval Figure 3 discloses labelled data being incorporated into a training set used to train a neural network.
With regards to Claim 2, Huval discloses the following limitations of Claim 1 and further discloses the following limitations:
“wherein the one or more processors configured to determine the one or more control signals are configured to: determine updates to one or more of: a steering angle of the vehicle; a speed of the vehicle; a rate of acceleration of the vehicle; or a rate of deceleration of the vehicle.”
Huval Paragraph [0018] discloses controlling all of these parameters.
With regards to Claim 5, Huval discloses the following limitations of Claim 1 and further discloses the following limitations:
“wherein the one or more processors configured to determine the one or more control signals to control operation of the vehicle based on the three-dimensional feature are configured to: determine a drivable space based on the three-dimensional feature; and determine the one or more control signals based on the drivable space.”
Huval Paragraph [0018] discloses by-passing obstacles and therefore identifies drivable space which the autonomous vehicle remains in.
With regards to Claim 6, Huval discloses the following limitations of Claim 1 and further discloses the following limitations:
“wherein the one or more processors configured to determine a three-dimensional feature associated with the sensor data based on a machine learning model are configured to determine a path of a vehicle in an adjacent lane relative to a lane of the vehicle; and wherein the one or more processors configured to determine the one or more control signals to control operation of the vehicle based on the three-dimensional feature are configured to: determine the one or more control signals to control operation of the vehicle based on the path of the vehicle in the adjacent lane.”
Huval Paragraph [0018] discloses identifying objects surrounding a vehicle including other vehicles which would include vehicles in adjacent lanes. This further includes identifying the movement of said other vehicles.
With regards to Claim 7, Huval discloses the following limitations of Claim 6 and further discloses the following limitations:
“wherein controlling operation of the vehicle based on the path of the vehicle in the adjacent lane involves adjusting a steering angle of the vehicle; a speed of the vehicle; a rate of acceleration of the vehicle; or a rate of deceleration of the vehicle to avoid contact between the vehicle and the vehicle in the adjacent lane.”
Huval Paragraph [0018] discloses by-passing surrounding obstacles including surrounding vehicles.
With regards to Claim 8, Huval discloses the following limitations of Claim 1 and further discloses the following limitations:
“wherein the one or more processors configured to determine the three-dimensional feature associated with the sensor data based on a machine learning model are configured to: determine the three-dimensional feature associated with the sensor data based on the machine learning model, wherein the machine learning model is trained using the training dataset comprising the determined ground truth and corresponding sensor data captured within a period of time, and wherein the machine learning model is trained to output the determined ground truth based on an input of at least a portion of the corresponding sensor data comprising a particular time series element of the plurality of time series elements.
Huval Paragraph [0008] discloses training a neural network using ground truth data that is first generated by a human operator, the neural network is then trained to output the ground truth data in the form of automated labels, S132 in Figure 4 in response to sensor information being received front he vehicle.
As per Claim 9: this claim is substantially similar to Claim 1 and is therefore rejected using the same references and rationale.
With regards to Claim 10, this claim is substantially similar to Claim 2 and is therefore rejected using the same references and rationale.
With regards to Claim 13, this claim is substantially similar to Claim 5 and is therefore rejected using the same references and rationale.
With regards to Claim 14, this claim is substantially similar to Claim 6 and is therefore rejected using the same references and rationale.
With regards to Claim 15, this claim is substantially similar to Claim 7 and is therefore rejected using the same references and rationale.
With regards to Claim 16, this claim is substantially similar to Claim 8 and is therefore rejected using the same references and rationale.
As per Claim 17: this claim is substantially similar to Claim 1 and is therefore rejected using the same references and rationale.
With regards to Claim 18, this claim is substantially similar to Claim 2 and is therefore rejected using the same references and rationale.
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 3-4, 11-12, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Huval in view of Schuh (US 2020/0201356).
With regards to Claim 3, Huval discloses all of the limitations of Claim 1 but does not disclose the following limitations that Schuh does disclose:
“wherein the one or more processors configured to determine the one or more control signals are configured to: determine the one or more control signals to maintain a position of the vehicle in a lane associated with the vehicle.”
Schuh Paragraphs [0090]-[0091] discloses using machine learning to autonomously control vehicles to maintain their position relative to other vehicles. Paragraph [0028] discloses that maintaining a lane in addition to relative position of surrounding vehicles is also accounted for.
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the system disclosed by Huval with the lane-keeping disclosed by Schuh. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to make the system more effective by allowing a vehicle to maintain its own position within a lane.
With regards to Claim 4, Huval discloses all of the limitations of Claim 1 but does not disclose the following limitations that Schuh does disclose:
“wherein the one or more processors configured to determine the one or more control signals are configured to: determine the one or more control signals to maintain a position of the vehicle in a lane associated with the vehicle relative to other vehicles or obstacles in proximity to the vehicle.”
Schuh Paragraphs [0090]-[0091] discloses using machine learning to autonomously control vehicles to maintain their position relative to other vehicles. Paragraph [0028] discloses that maintaining a lane in addition to relative position of surrounding vehicles is also accounted for.
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the system disclosed by Huval with the lane-keeping disclosed by Schuh. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to make the system more effective by allowing a vehicle to maintain its own position within a lane.
With regards to Claim 11, this claim is substantially similar to Claim 4 and is therefore rejected using the same references and rationale.
With regards to Claim 12, this claim is substantially similar to Claim 5 and is therefore rejected using the same references and rationale.
With regards to Claim 19, this claim is substantially similar to Claim 4 and is therefore rejected using the same references and rationale.
With regards to Claim 20, this claim is substantially similar to Claim 5 and is therefore rejected using the same references and rationale.
Related References
Theverapperuma (US 2020/0394813)
Related to volumetric estimation using machine learning.
Frisbie (US 2020/0393841)
Related to vehicle control using machine learning.
Gurel (US 2020/0364508)
Related to lane identification based on machine learning.
Jung (US 2020/0116499)
Related to vehicle localization.
Ghafarianzadeh (US 2019/0250626)
Related to vehicle control and machine learning.
Chen (US 2019/0212749)
Related to vehicle control using machine learning.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Examiner Godfrey Maciorowski, whose telephone number is (571) 272-4652. The examiner can normally be reached on Monday-Friday from 7:30am to 5:00pm EST.
Examiner interviews are available via telephone and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach examiner by telephone are unsuccessful the examiner’s supervisor, Thomas Worden can be reached on (571) 272-4876. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/GODFREY ALEKSANDER MACIOROWSKI/Examiner, Art Unit 3658 /JASON HOLLOWAY/Primary Examiner, Art Unit 3658
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