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 Arguments
1. Applicant's arguments filed 26 November 2025 have been fully considered but are not persuasive. The new limitations are disclosed by at least Levandowski and/or Lee as detailed in the rejection below.
Claim Rejections - 35 USC § 103
2. 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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
3. Claims 1-8 rejected under 35 U.S.C. 103 as being unpatentable over Levandowski et al. (U.S. Patent Application Publication # US 2023/0084316) in view of Lee et al. (U.S. Patent Application Publication # US 2023/0315085)
Regarding claims 1 and 7, Levandowski discloses a method for managing autonomous driving function in a vehicle (abstract), the method comprising the steps of:
a management server (122 or 416, see fig. 2B and 4 and par. [0099], [0117] - [0123], etc) transmitting data for updating indicating data to update a trained model (220, see par. [0102]: "the application server 122 can provide models to the vehicles 104 and/or receive image data to update the models", etc) to at least two vehicles on which autonomous driving control are performed by using the trained model (see par. [0097], fig. 2A, 4, etc), the trained model indicating a model generated by a machine learning (abstract);
the management server collecting data for verification from each of the at least two vehicles to which the data for updating have been transmitted (see fig.19A, 19B and 20, par.[0208]-[0215]: "a number of course deviations and/or corrections may be utilized to determine if a new autonomous vehicle model 452 is needed and/or should be updated', etc),
the data for verification indicating data comprising data captured by one or more sensors (fig. 4, [0117]-[0119], etc), a target trajectory (fig. 20: 2024, etc), a control amount to follow the target trajectory (fig. 20: 2028, etc), and a recognition result of the one or more sensors (fig. 19A, P208, 243, etc: sensor data collected from vehicles, then server processes sensor data to obtain recognition result, but server does not directly collect recognition result from vehicles) obtained or generated in connection with the performance with the autonomous driving control by using an updated trained model indicating the trained model updated by the data for updating (see fig. 20, par.[0215], etc) (please note that the limitation “indicating data” broadens the claim and suggests that the data for verification does not require “data captured by one or more sensors, a target trajectory, a control amount to follow the target trajectory, and a recognition result of the one or more sensors”, but requires only data that indicates “data captured by one or more sensors, a target trajectory, a control amount to follow the target trajectory, and a recognition result of the one or more sensors”); and
the management server verifying the function of the autonomous driving control in which the updated trained model is used, by using the data for verification that have been collected from the at least two vehicles (see par.[0209]: "a statistical accuracy of the model may be monitored over time. For example, as a result of the output prediction at 560 and/or the output of the vehicle adjust at 464, the number of and type of user engagements and/or disengagements may be monitored and correlated with an output accuracy of the autonomous vehicle model. Such correlating may occur at step 1936. In accordance with embodiments of the present disclosure, the statistical accuracy of the model from step 1936 may be utilized to determine if a new model 452 may need to be obtained', etc).
In other words, Levandowski discloses the management server collecting data for verification from each of the at least two vehicles (see citations above), and the data for verification indicating data comprising a target trajectory, a control amount to follow the target trajectory, and a recognition result of the one or more sensors (see citations above), but does not explicitly disclose the (implied) combined feature of collecting (directly) from the vehicles: a target trajectory, a control amount to follow the target trajectory, and a recognition result of the one or more sensors.
In the same field of endeavor, Lee discloses collecting from the vehicle(s), a target trajectory and a recognition result of the one or more sensors (P52, etc). Levandowski as modified by Lee would teach collecting from the vehicles a control amount to follow the target trajectory (fig. 20: 2028, etc; Lee P52, etc).
It would have been obvious before the effective filing date of the claimed invention to modify Levandowski to collect the data from the vehicles, as suggested by Levandowski and/or taught by Lee, in order to optimally distribute processing load between the vehicle and server, with predictable results.
Regarding claims 2, 4, and 8, Levandowski further discloses the steps of:
a processor configured to perform the autonomous driving control (see par.[0121], etc) of a management object included in the at least two vehicles collecting data obtained or generated in connection with the performance of the autonomous driving control in the management object as the data for verification regarding the management object during the autonomous driving control using the updated trained model (see fig.19A, 19B and 20, par. [0121], [0208]-[0215], etc), the data for verification collected during a predetermined collection time starting from a reference timing at which the predetermined collection condition is satisfied; and the processor transmitting the data for verification regarding the management object to the management server (see par.[0121], par.[0209], in part.: "In accordance with embodiments of the present disclosure, a statistical accuracy of the model may be monitored over time. For example, as a result of the output prediction at 560 and/or the output of the vehicle adjust at 464, the number of and type of user engagements and/or disengagements may be monitored and correlated with an output accuracy of the autonomous vehicle model', etc. This implies data collection necessarily started at a predetermined collection condition).
Regarding claim 3, Levandowski further discloses the predetermined collection condition includes a condition that there has been an intervention to the autonomous driving control by an operator of the management object (see par.[0209] and [2014]-[0215], etc), wherein the method further comprises the step of variably setting a length of the predetermined collection time based on a timing at which the collection condition is satisfied, and based on a driving proficiency of the operator of the management object (well known in the art, etc).
Regarding claim 5-6, Levandowski further discloses that the predetermined collection condition includes a condition that there has been an intervention to the autonomous driving control by an operator of the management object, wherein, in the step of verifying the autonomous driving function, number of interventions for the autonomous driving control is used as a verification index, the interventions obtained during a predetermined verification period after transmitting the data for updating or until a driving distance of the management object reaches a predetermined verification distance after transmitting the data for updating (see par.[0209] and [2014]-[0215], etc).
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHELLEY CHEN whose telephone number is (571)270-1330. The examiner can normally be reached Mondays through Fridays.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Erin Bishop can be reached at (571) 270-3713. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Shelley Chen/
Patent Examiner
Art Unit 3665
January 20, 2026