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 .
Status of Claims
Claims 1-15 are rejected under 35 U.S.C. 103 as being unpatentable under Kabirzadeh (US 11,150,660) in view of Crego (US 11,741,274).
Priority
The priority date for this application is 10/28/2022.
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.
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 1-15 are rejected under 35 U.S.C. 103 as being unpatentable over Kabirzadeh in view of Crego. These references are analogous as they are both related to simulation of trajectories for the purposes of autonomous vehicle control (see [Abstract] of both references).
As per Claim 1:
Kabirzadeh discloses the following limitations:
“A computer implemented method for generating trajectory information of a plurality of road users, the method comprising: determining a cost function which maps the trajectory information of the plurality of road users to a cost value”
Kabirzadeh Column 4 Line 62-Column 5 Line 23 discloses associating trajectories with cost values. (See Also Column 2 Lines 27-35).
“determining a side constraint function which maps the trajectory information of the plurality of road users to a side constraint value”
Kabirzadeh Column 4 Line 62-Column 5 Line 23 discloses multiple types of cost (i.e. cost or time) either one of these can arbitrarily represent a "side constraint".
“and solving an optimization problem for the trajectory information based on the cost function and based on the side constraint function”
Kabirzadeh Column 21 Lines 33-46 discloses optimizing costs.
“and by a vehicle, based on the trajectory information, performing at least one of driving assistance and autonomous driving.”
Kabairzadeh Column 1 Lines 52-65 teach controlling an autonomous vehicle based on trajectory information.
Kabirzadeh does not disclose the following limitations that Crego suggests:
“wherein the trajectory information of the plurality of road users comprises a plurality of parameters, wherein the plurality of parameters define a respective actual trajectory for each road user of the plurality of road users and a respective observed trajectory for each road user of the plurality of road users, wherein the respective observed trajectory for each road user of the plurality of road users represents how the respective actual trajectory for each road user of the plurality of road users is observed by a sensor; and wherein the cost function and/ or the side constraint function comprises a term based on both the actual trajectory or trajectories for at least one road user of the plurality of road users and the corresponding observed trajectory or trajectories for the at least one road user of the plurality of road users.”
Crego Column 26 Lines 48-62 teach comparing perceived trajectories to actual trajectories in a simulated environment.
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the system disclosed by Kabirzadeh with the actual and perceived trajectories suggested by Crego. 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 accurate by comparing results.
With regards to Claim 5, Kabirzadeh in view of Crego discloses all of the limitations of Claim 1 and further discloses the following limitations:
“wherein the parameters further comprise static environment parameters.”
Kabirazadeh Column 2 Lines 44-67 discloses simulations containing static environments.
With regards to Claim 6, Kabirzadeh in view of Crego discloses all of the limitations of Claim 1 and further discloses the following limitations:
“wherein the cost function and/ or the side constraint function is based on a severity of a scenario represented by the trajectory information.”
Kabirazadeh Column 17 Lines 1-23 discloses filtering out (i.e. assigning an infinite cost) to objects based on the "level of interaction". In scenarios in which an object has a lower "level of interaction" with the instant vehicle the impact such an object would have would be less severe given the broadest reasonable interpretation of that term and therefore represents the limitation presented in this claim.
With regards to Claim 7, Kabirzadeh in view of Crego discloses all of the limitations of Claim 1 and further discloses the following limitations:
“wherein the cost function and/ or the side constraint function is based on a plausibility of a scenario represented by the trajectory information.”
Kabirazadeh Column 17 Lines 1-23 discloses filtering out (i.e. assigning an infinite cost) to objects based on the "confidence level". Such a confidence level represents a likelihood that an object has been accurately identified, therefore filtering out objects (an correspondingly scenario involving said objects) represents a filtering based on the likelihood that the scenario is accurate (i.e. the plausibility of the scenario).
With regards to Claim 9, Kabirzadeh in view of Crego discloses all of the limitations of Claim 1 and further discloses the following limitations:
“wherein the cost function and/ or the side constraint function comprises a term related to a desired output of a scenario represented by the trajectory information.”
Kabirazadeh Column 14 Lines 9-23 discloses prioritizing trajectories that result in safe stops avoiding collisions.
With regards to Claim 10, Kabirzadeh in view of Crego discloses all of the limitations of Claim 1 and further discloses the following limitations:
“further comprising the following step carried out by the computer hardware components: training a machine-learning model for driving assistance based on the trajectory information.”
Kabirazadeh Column 20 Lines 4-8 discloses incorporating machine learning models into the system disclosed, the implementation of machine learning models would require their training as well.
With regards to Claim 11, Kabirzadeh in view of Crego discloses all of the limitations of Claim 1 and further discloses the following limitations:
“further comprising the following step carried out by the computer hardware components :testing a machine-learning model for driving assistance based on the trajectory information.”
Kabirazadeh Column 20 Lines 4-8 discloses incorporating machine learning models into the system disclosed, the implementation of machine learning models would require their testing as well.
With regards to Claim 12, Kabirzadeh discloses all of the limitations of Claim 10 and further discloses the following limitations:
“wherein the training and/ or the testing comprises evaluating a driving policy for an at least partially autonomous vehicle.”
Kabirazadeh [Abstract] discloses using the simulation scenarios to test and validate vehicle controller behaviors.
With regards to Claim 13, Kabirzadeh discloses all of the limitations of Claim 12 and further discloses the following limitations:
“The computer implemented method according to wherein the driving policy acts based on observed trajectories for the plurality of road users; and wherein the driving policy is evaluated based on actual trajectories for the plurality of road users.”
Kabirazadeh Column 22 Lines 26-45 discloses evaluating simulation behaviors compared to real-world data.
As per Claim 14: this claim is substantially similar to Claim 1 and is therefore rejected using the same references and rationale.
As per Claim 15: this claim is substantially similar to Claim 1 and is therefore rejected using the same references and rationale.
Claims 2-3 are rejected under 35 U.S.C. 103 as being unpatentable over Kabirazadeh in view of Crego in view of Alesiani (US 2019/0272752).
With regards to Claim 2, Kabirazadeh in view of Crego discloses all of the limitations of Claim 1 but does not disclose the following limitations that Alesiani does disclose:
“wherein the optimization problem is solved iteratively.”
Alesiani Paragraph [0014] discloses iteratively solving an optimization problem.
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the system disclosed by Kabirazadeh with the iterative solving of optimization problems disclosed by Alesiani. 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 utilizing converging solving techniques.
With regards to Claim 3, Kabirazadeh in view of Crego in view of Alesiani discloses all of the limitations of Claim 2 and further discloses the following limitations:
“wherein an initial trajectory information for the optimization problem is determined randomly.”
Alesiani Paragraph [0042] discloses setting random initial values for optimization problems.
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the system disclosed by Kabirazadeh in view of Crego with the initial value randomization disclosed by Alesiani. 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 eliminating bias in sampling for the simulation
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Kabirazadeh in view of Crego in view of Bonawitz (US 2014/0188377).
With regards to Claim 4, Kabirazadeh in view of Crego discloses all of the limitations of Claim 1 but does not disclose the following limitations that Bonawitz does disclose:
“wherein the optimization problem is solved based on a gradient-free stochastic method, preferably a particle swarm optimization method or a covariance matrix adaptation evolution method.”
Bonawitz Paragraph [0100] discloses using stochastic optimization as an alternative to gradient based methods.
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the system disclosed by Kabirazadeh in view of Crego with the optimization techniques disclosed by Bonawitz. 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 utilizing known problem solving mechanics.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Kabirazadeh in view of Crego in view of Pedersen (US 2022/0198107).
With regards to Claim 8, Kabirazadeh in view of Crego discloses all of the limitations of Claim 1 but does not disclose the following limitations that Pedersen does disclose:
“wherein the cost function and/ or the side constraint function is based on a novelty of a scenario represented by the trajectory information.”
Pedersen Paragraph [0027] discloses prioritizing new scenarios for vehicle trajectory simulation.
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the system disclosed by Kabirazadeh in view of Crego with the novelty calculations disclosed by Pedersen. 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 preferentially avoiding simulations of situations that have already been explored.
Conclusion
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).
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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