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 .
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.
Claims 1-14, 16, 17, 19-22 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Liu (US 20180275667 A1).
Regarding claim 1, Liu discloses a performance analysis computing system for analyzing autonomous vehicle fleet performance based on collected feedback (figs. 3-6; abstract; sec 0001, 0004, 0016, 0017), the computing system comprising:
one or more processors (104, 106, 112, 132; figs. 2-4; sec 0098, 0099, 0100); and
one or more non-transitory computer-readable media that store instructions that are executable by the one or more processors to cause the performance analysis computing system to perform operations (sec 0098, 0100), the operations comprising:
accessing vehicle performance feedback data from a plurality of autonomous vehicles (sec 0028, 0048, 0049, 0078), wherein the vehicle performance feedback data comprises, for a respective vehicle of the plurality of autonomous vehicles:
respective feedback data associated with an event and from a respective interface device associated with the respective autonomous vehicle (sec 0028, 0048, 0049, 0078); and
respective vehicle data associated with the respective feedback data and collected by the respective autonomous vehicle over a time window prior to the event (i.e. feedback data implies collecting data over a time window prior to the event; figs. 3, 5, 6; sec 0028, 0048, 0049, 0078);
training, based on the vehicle performance feedback data, a machine- learned model of an event detector (108; fig. 4) to classify events (sec 0016, 0023, 0041, 0045-0049, 0094, 0095); and
providing the event detector to an autonomy computing system of an autonomous vehicle (102, 130; fig. 4), the autonomy computing system configured to provide data from sensors of the autonomous vehicle to the event detector to detect events occurring during control of the autonomous vehicle by the autonomy computing system (sec 0016, 0023, 0041, 0045-0049, 0094, 0095).
Regarding claim 2, Liu discloses the computing system of claim 1, wherein the vehicle performance feedback data comprises one or more data dimensions, and wherein the vehicle performance feedback data is aggregated over the one or more data dimensions (sec 0016, 0023, 0041, 0045-0049, 0094, 0095).
Regarding claim 3, Liu discloses the computing system of claim 1, the operations comprising:
computing one or more measures over the aggregated vehicle performance feedback data (sec 0016, 0023, 0041, 0045-0049, 0094, 0095); and
outputting an aggregate performance score (sec 0054, 0066) based on the computed one or more measures over the aggregated vehicle performance feedback data (sec 0016, 0023, 0041, 0045-0049, 0094, 0095).
Regarding claim 4, Liu discloses the computing system of claim 1, wherein the respective vehicle data comprises sensor data (sec 0016, 0023, 0041, 0045-0049, 0094, 0095).
Regarding claim 5, Liu discloses the computing system of claim 4, wherein the sensor data comprises at least one of LIDAR data, image data (sec 0023, 0026, 0030), or RADAR data.
Regarding claim 6, Liu discloses the computing system of claim 4, wherein the sensor data comprises LIDAR data and image data (sec 0023, 0026, 0030).
Regarding claim 7, Liu discloses the computing system of claim 4, wherein the sensor data comprises at least one data type selected from: accelerometer data, positioning system data, gyroscope data, throttle position data, engine torque data, crankshaft torque data, exhaust oxygen data, engine air flow data, engine RPM data, or vehicle control data (sec 0016, 0023, 0026, 0030, 0045-0049) associated with a vehicle controller.
Regarding claim 8, Liu discloses the computing system of claim 1, the operations comprising:
receiving, from an interactive user interface associated with operation of the respective vehicle, feedback for the event (sec 0016, 0023, 0041, 0045-0049, 0094, 0095).
wherein the respective feedback data is based on the feedback (sec 0016, 0023, 0041, 0045-0049, 0094, 0095).
Regarding claim 9, Liu discloses the computing system of claim 1, wherein the event comprises a human intervention event in which a human driver assumed control of the respective vehicle (sec 0016, 0023, 0041, 0045-0049, 0094, 0095).
Regarding claim 10, Liu discloses the computing system of claim 1, the operations comprising:
obtaining, from the vehicle performance feedback data and based on the associations between the respective vehicle data and the respective feedback data, a subset characterized by one or more dimensions of the respective feedback data or one or more dimensions of the respective vehicle data (sec 0016, 0023, 0041, 0045-0049, 0094, 0095).
Regarding claim 11, Liu discloses the computing system of claim 1, wherein the respective feedback data comprises human perception information regarding autonomous driving behavior (sec 0016, 0023, 0041, 0045-0049, 0094, 0095).
Regarding claim 12, Liu discloses the computing system of claim 1, wherein the respective feedback data comprises textual human perception information regarding autonomous driving behavior (sec 0016, 0023, 0041, 0045-0049, 0094, 0095).
Regarding claim 13, Liu discloses a computer-implemented method, comprising:
providing, by an autonomy computing system configured to control an autonomous vehicle, available data from sensors of the autonomous vehicle to an event detector, the event detector comprising a machine-learned model trained based on vehicle performance feedback data from a plurality of autonomous vehicles (sec 0016, 0023, 0041, 0045-0049, 0094, 0095), wherein the vehicle performance feedback data comprises, for a respective vehicle of the plurality of autonomous vehicles:
respective feedback data associated with an event and from a respective interface device associated with the respective autonomous vehicle (sec 0016, 0023, 0041, 0045-0049, 0094, 0095); and
respective vehicle data associated with the respective feedback data and collected by the respective autonomous vehicle over a time window prior to the event (sec 0016, 0023, 0041, 0045-0049, 0094, 0095); and
detecting, by the autonomy computing system and based on the event detector, a vehicle event occurring during the control of the autonomous vehicle (sec 0016, 0023, 0041, 0045-0049, 0094, 0095).
Regarding claim 14, Liu discloses the method of claim 13, wherein the vehicle performance feedback data comprises one or more data dimensions, and wherein aggregating the vehicle performance feedback data is aggregated comprises aggregating the vehicle performance feedback data over the one or more data dimensions (sec 0016, 0023, 0041, 0045-0049, 0094, 0095).
Regarding claim 16, Liu discloses the method of claim 13, wherein the respective vehicle data comprises sensor data, and wherein the sensor data comprises LIDAR data and image data (sec 0016, 0023, 0041, 0045-0049, 0094, 0095).
Regarding claim 17, Liu discloses the method of claim 13, wherein the event comprises a human intervention event in which a human driver assumed control of the respective vehicle (sec 0016, 0023, 0041, 0045-0049, 0094, 0095).
Regarding claim 19, Liu discloses an autonomy computing system configured to control an autonomous vehicle (sec 0016, 0023, 0041, 0045-0049, 0094, 0095), the autonomy computing system comprising:
an event detector configured to detect occurrence of events during control of the autonomous vehicle by the autonomy computing system (sec 0016, 0023, 0041, 0045-0049, 0094, 0095), the event detector comprising a machine-learned model trained based on:
vehicle performance feedback data from a plurality of autonomous vehicles (sec 0016, 0023, 0041, 0045-0049, 0094, 0095), wherein the vehicle performance feedback data comprises, for a respective vehicle of the plurality of autonomous vehicles:
respective feedback data associated with an event and from a respective interface device associated with the respective autonomous vehicle (sec 0016, 0023, 0041, 0045-0049, 0094, 0095); and
respective vehicle data associated with the respective feedback data and collected by the respective autonomous vehicle over a time window prior to the event (sec 0016, 0023, 0041, 0045-0049, 0094, 0095).
Regarding claim 20, Liu discloses the autonomy computing system of claim 19, wherein the event comprises a human intervention event in which a human driver assumed control of the respective vehicle (sec 0016, 0023, 0041, 0045-0049, 0094, 0095).
Regarding claim 21, Liu discloses the autonomy computing system of claim 19, wherein the respective vehicle data comprises sensor data, and wherein the sensor data comprises LIDAR data and image data (sec 0016, 0023, 0041, 0045-0049, 0094, 0095).
Regarding claim 22, Liu discloses the autonomy computing system of claim 19, wherein the respective vehicle data comprises at least one data type selected from:
accelerometer data, positioning system data, gyroscope data, throttle position data, engine torque data, crankshaft torque data, exhaust oxygen data, engine air flow data, engine RPM data, or vehicle control data associated with a vehicle controller (sec 0016, 0023, 0041, 0045-0049, 0094, 0095).
Response to Arguments
Applicant’s arguments with respect to claims have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Communication
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RONNIE MANCHO whose telephone number is (571)272-6984. The examiner can normally be reached Mon-Thurs.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Adam Mott can be reached at 571 270 5376. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RONNIE M MANCHO/Primary Examiner, Art Unit 3657
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