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 04/17/2026. Claims 1, 3-11, 13-16, and 18-20 are considered in this office action. No claims have been amended. Claims 2, 12, and 17 have been cancelled. Claims 1, 3-11, 13-16, and 18-20 are pending examination. This action is made final.
Response to Arguments
Applicant presents the following arguments regarding the previous office action:
“While the DELLA PENNA system results in updating the autonomous driving model, similar to the invention claimed in this application, DELLA PENNA does not include any reference to determining if the deviations exceed a threshold. Additionally, DELLA PENNA does not teach or suggest determining if the deviations exceed a threshold within a period of time."
Applicant's argument A. has been fully considered but it is not persuasive.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Specifically, regarding Applicant’s argument A. that “While the DELLA PENNA system results in updating the autonomous driving model, similar to the invention claimed in this application, DELLA PENNA does not include any reference to determining if the deviations exceed a threshold. Additionally, DELLA PENNA does not teach or suggest determining if the deviations exceed a threshold within a period of time," Examiner notes that cited reference Zheng, not Della Penna, was used to teach the claim limitations regarding determining if the deviations exceed a threshold within a period of time. Zheng 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 (Zheng, Par. [0074]), however Zheng does not explicitly teach using “course corrections” or “course deviations” when detecting the condition associated with the performance of the first autonomous vehicle driving model. Examiner notes that the instant specification in Par. [0201] describes “a course correction includes determining that an input associated with a manual override was received” and “a course deviation includes determining that a path traveled by an autonomous vehicle is different from a projected path traveled by the autonomous vehicle.” Della Penna teaches an event recorder 156 configured to collect streams of data to track and analyze data coinciding at or substantially coextensive with events of interest, where the analysis is used to update the logic of software of autonomy controller 150 of autonomous vehicle 120 by generating enhanced or refined rules of operation (e.g., updating autonomy controller logic) (Della Penna, Par. [0024]). An event of interest may be an instance during which human input (i.e., manual intervention) overrides autonomy controller 150 or autonomous operation to deviate (e.g., by a threshold range of expected values or actions) from one or more trajectories or courses of action computed by autonomy controller 150 (Della Penna, Par. [0025]). Both Zheng and Della Penna teach using anomalous events detected while driving to determine if a current driving model should be updated based on those events. Thus, it would have been obvious to one of ordinary skill in the art to modify the teachings of Zheng to incorporate the teachings of Della Penna with a reasonable expectation of success to have, when determining that the first autonomous vehicle driving model should be updated, the quantity used when detecting a condition associated with a performance of the first autonomous vehicle driving model as taught by Zheng be course deviation or course correction events as taught by Della Penne. 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]). Therefore, Examiner maintains that the combination of Zheng and Della Penna teaches the above stated claim limitations.
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 using “course corrections” or “course deviations” when detecting the condition associated with the performance of the first autonomous vehicle driving model.
From the same field of endeavor regarding updating a driving model, Della Penna teaches using “course corrections” or “course deviations” when detecting the condition associated with the performance of the first autonomous vehicle driving model (Par. [0024] teaches an event recorder 156 configured to collect streams of data to track and analyze data coinciding at or substantially coextensive with events of interest, where the analysis is used to update the logic of software of autonomy controller 150 of autonomous vehicle 120 by generating enhanced or refined rules of operation (e.g., updating autonomy controller logic); Par. [0025] teaches an event of interest may be an instance during which human input (i.e., manual intervention) overrides autonomy controller 150 or autonomous operation to deviate (e.g., by a threshold range of expected values or actions) from one or more trajectories or courses of action computed by autonomy controller 150; 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 have, when determining that the first autonomous vehicle driving model should be updated, the quantity used when detecting a condition associated with a performance of the first autonomous vehicle driving model as taught by Zheng be course deviation or course correction events as taught by Della Penne.
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
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 KATHERINE M FITZHARRIS whose telephone number is (469)295-9147. The examiner can normally be reached 7:30 am - 6:00 pm M-Th.
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/K.M.F./Examiner, Art Unit 3665
/CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665
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