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 .
Response to Amendment
This action is in response to amendments and remarks filed on 12/09/2025. Claims 1, 3-11, 13-16, and 18-20 are considered in this office action. Claims 1, 3, 5-7, 9-11, 13, 15-16, 18, and 20 have been amended. Claims 2, 12, and 17 have been cancelled. Claims 1, 3-11, 13-16, and 18-20 are pending examination. Objections to the specification and claims 1, 11, and 16 and the 35 U.S.C. 112(b) rejections of claims 5, 9-10, 15, and 20 have been withdrawn in light of the instant amendments.
Response to Arguments
Applicant presents the following arguments regarding the previous office action:
“[T]his document [SHALEV-SCHWARTZ] does not teach or suggest the following phrase from claim 2 that is now included in each of the independent claims "wherein detecting the condition associated with the performance of the first autonomous vehicle driving model includes determining that at least one of a quantity of course corrections or a quantity of course deviations exceeded a threshold within a period of time."
Applicant’s argument A. with respect to the rejection(s) of the claim(s) under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Della Penna (US 2019/0220011 A1) as described below under Claim Rejections.
Claim Rejections - 35 USC § 103
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, 3-11, 13-16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Zheng et al. (US 2018/0349782 A1) in view of Della Penna (US 2019/0220011 A1).
Regarding claim 1, Zheng teaches “A method comprising:
receiving a first autonomous vehicle driving model (Fig. 1 shows autonomous vehicles 110-1,…,110-N receiving vehicle models);
detecting a condition associated with a performance of the first autonomous vehicle driving model (Par. [0009] teaches sensor data acquired continuously by sensors deployed on the vehicle is first received, where the sensor data provides information about surrounding of the vehicle, and one or more items surrounding the autonomous driving vehicle are tracked, based on some models, from the sensor data acquired by one or more of a first type of the plurality of types of sensors. Some of the items are labeled, automatically on-the-fly, via at least one of cross modality validation and cross temporal validation of the one or more items), wherein detecting the condition associated with the performance of the first autonomous vehicle driving model includes determining that a quantity of [events] exceeded a threshold within a period of time (Par. [0074] teaches events of interest are processed and selected, then used to update local and/or global models, where the model adaptation may be scheduled based on, e.g., some fixed time intervals, some criterion such as when the events of interest selected have accumulated to a pre-determined volume (i.e., exceeded a threshold within a period of time), or when some events of interest selected suggest an error that needs to be corrected);
determining that the first autonomous vehicle driving model should be updated based on the detected condition (Par. [0009] teaches at least one of the labeled items is sent to a model update center, and the model update information is derived based on the at least one of the labeled items; Par. [0008] teaches based on the received labeled data items, at least some of the models are updated and model update information is generated);
receiving at least one update for the first autonomous vehicle driving model (Par. [0009] teaches model update information is received from the model update center); and
applying the at least one update to the first autonomous vehicle driving model (Par. [0009] teaches the at least one model is updated in accordance with the model update information) to generate a second autonomous vehicle driving model based on the at least one update (Par. [0009] teaches the at least one model is updated in accordance with the model update information (implying that a first model is updated to a different second model)).”
However, Zheng does not explicitly teach detecting the condition associated with the performance of the first autonomous vehicle driving model includes determining that at least one of a quantity of “course corrections” or “course deviations” exceeded a threshold.
From the same field of endeavor, Della Penna teaches detecting the condition associated with the performance of the first autonomous vehicle driving model includes determining that at least one of a quantity of “course corrections” or “course deviations” exceeded a threshold (Par. [0029] teaches event recorder 156 detects that applied vehicular drive parameter (i.e., human input) is being applied to alter course into lane 113 and identifies the conflicting courses of action as an event, and event-adaptive computing platform 109 evaluates a subset of signals representing event data, including comparing the subset of signals against one or more patterns of similar event data to identify modifications to autonomy controller 150).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to modify the teachings of Zheng to incorporate the teachings of Della Penna with a reasonable expectation of success to use course corrections or deviations, as taught by Della Penna, as the event used when determining a quantity has exceeded a threshold for detecting the condition associated with the performance of the first autonomous vehicle driving model as taught by Zheng.
The motivation for doing so would be to determine an optimal subset of actions or rules autonomy controller may implement in similar subsequent situations (Della Penna, Par. [0029]).
Regarding claim 11 and claim 16, the limitations of this system claim and this non-transitory computer readable medium claim, respectively, are rejected using the combination of cited references Zheng and Della Penna based on the exemplary analysis of the method claim 1 above as the limitations of system claim 11 and non-transitory computer readable medium claim 16 are commensurate in scope to the limitations of rejected method claim 1.
Regarding claim 3, the combination of cited references Zheng and Della Penna teaches all the limitations of claim 1 above, and further teaches “wherein a course correction includes determining that an input associated with a manual override was received (Della Penna, Par. [0029] teaches event recorder 156 detects that applied vehicular drive parameter (i.e., human input) is being applied to alter course into lane 113 and identifies the conflicting courses of action as an event).”
Regarding claim 13 and claim 18, the limitations of this system claim and this non-transitory computer readable medium claim, respectively, are rejected using the combination of cited references Zheng and Della Penna based on the exemplary analysis of the method claim 3 above as the limitations of system claim 13 and non-transitory computer readable medium claim 18 are commensurate in scope to the limitations of rejected method claim 3.
Regarding claim 4, the combination of cited references Zheng and Della Penna teaches all the limitations of claim 3 above, and further teaches “wherein the input associated with the manual override includes one or a manual velocity change or manual steering angle change (Della Penna, Par. [0029] teaches event recorder 156 detects that applied vehicular drive parameter (i.e., human input), such as a steering wheel angle, is being applied to alter course into lane 113 and identifies the conflicting courses of action as an event).”
Regarding claim 14 and claim 19, the limitations of this system claim and this non-transitory computer readable medium claim, respectively, are rejected using the combination of cited references Zheng and Della Penna based on the exemplary analysis of the method claim 4 above as the limitations of system claim 14 and non-transitory computer readable medium claim 19 are commensurate in scope to the limitations of rejected method claim 4.
Regarding claim 5, the combination of cited references Zheng and Della Penna teaches all the limitations of claim 1 above, and further teaches “wherein a course deviation includes determining that a path to be traveled by an autonomous vehicle is different from a projected path traveled by the autonomous vehicle (Della Penna, Par. [0025] teaches an event of interest is an instance during which human input overrides autonomy controller 150 or autonomous operation to deviate from one or more trajectories computed by autonomy controller 150).”
Regarding claim 15 and claim 20, the limitations of this system claim and this non-transitory computer readable medium claim, respectively, are rejected using the combination of cited references Zheng and Della Penna based on the exemplary analysis of the method claim 5 above as the limitations of system claim 15 and non-transitory computer readable medium claim 20 are commensurate in scope to the limitations of rejected method claim 5.
Regarding claim 6, the combination of cited references Zheng and Della Penna teaches all the limitations of claim 1 above, and further teaches “wherein the threshold varies based on one or more of time, location, date, or weather condition (Zheng, Par. 0060] teaches synchronization between the global model update cloud 160 and the fleet may be divided based on different considerations including time zones, operating environment (location), and weather; Par. [0097]-[0098] and [0115] teach using time-stamped data).”
Regarding claim 7, the combination of cited references Zheng and Della Penna teaches all the limitations of claim 1 above, and further teaches “wherein the at least one update includes one or more model parameters for a portion of the first autonomous vehicle driving model (Zheng, Par. [0009] teaches at least one of the labeled items is sent to a model update center, and the model update information is derived based on the at least one of the labeled items; Par. [0008] teaches based on the received labeled data items, at least some of the models are updated and model update information is generated).”
Regarding claim 8, the combination of cited references Zheng and Della Penna teaches all the limitations of claim 1 above, and further teaches “recording a location associated with one or more course corrections and/or course deviations (Della Penna, Par. [0065] teaches receiving contextual data, such as geographic data such as locations (e.g., GPS coordinates)).”
Regarding claim 9, the combination of cited references Zheng and Della Penna teaches all the limitations of claim 1 above, and further teaches “wherein the second autonomous vehicle driving model is received at an autonomous vehicle (Zheng, Par. [0008] teaches generated model update information is distributed to (i.e. received by) the autonomous driving vehicles).”
Regarding claim 10, the combination of cited references Zheng and Della Penna teaches all the limitations of claim 1 above, and further teaches “wherein the second autonomous vehicle driving model is generated at an autonomous vehicle (Zheng, Par. [0009] teaches when model update information is received from the model update center, the at least one model is updated).”
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KATHERINE M FITZHARRIS whose telephone number is (469)295-9147. The examiner can normally be reached 7:30 am - 6:00 pm M-Th.
Examiner interviews are available via telephone, in-person, 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 the examiner by telephone are unsuccessful, the examiner’s supervisor, CHRISTIAN CHACE can be reached at (571)272-4190. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/K.M.F./Examiner, Art Unit 3665
/CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665