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
Examiner’s Response re: 103 Rejection
Applicant’s arguments, see Pages 6 - 8, filed 01 Jul 2026, with respect to the rejection(s) of claim(s) 1 - 20 under 103 Rejection 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 Therefore, the rejection has been withdrawn.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 Sri, and Huang.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Srinivasan et al., US 20200239023 A1 (herein, Sri) and in view of Huang et al., US 20190302761 A1 (herein, Huang).
Regarding Claims 11 and 1, Sri discloses, an autonomous vehicle comprising (Abstract – “autonomous vehicle”):
a plurality of sensors operable to produce sensor data of at least one of operation of the autonomous vehicle and an environment (¶[0003] – “An autonomous vehicle is a vehicle that is capable of sensing its environment and operating some or all of the vehicle's controls based on the sensed environment. Autonomous vehicles include a control system that receives information from a variety of on-board sensors…”);
a vehicle control system operable to move the autonomous vehicle within the environment (¶[0024] – “…, a vehicle control system for an autonomous vehicle may not be capable of successfully controlling the vehicle through all situations the vehicle…”);
an autonomous vehicle model trained on autonomously controlling the autonomous vehicle within the environment (¶[0067] – “… the prediction system 304 can include a scenario generation system that generates and/or scores the one or more goals for an object, and a scenario development system…a machine-learned goal-scoring model, a machine-learned trajectory development model, and/or other machine-learned models.”) ;
one or more processors (FIG. 15 and #1502 – processor); and
a non-transitory computer-readable medium storing instructions that, when executed by the one or more processors (¶[0187] – “… a non-transitory computer readable storage medium comprising instructions that when executed configure …”), to:
autonomously control the autonomous vehicle within the environment using the autonomous vehicle model and the vehicle control system (¶[0031] – “… it can be complex and equally costly to ensure that automated vehicle control systems can successful manage each and every situation that may be encountered by an autonomous vehicle during its lifetime….”);
receive control signals from the remote operator (¶[0034] – “…receive input from the operator and provide control signals to the autonomous vehicle 102…”);
control, using the vehicle control system, the autonomous vehicle according to the control signals from the remote operator (¶[0031] – “…by providing the on-demand operator assistance, the use of both autonomous control systems and human operators…”).
Sri discloses the sensor data but does not disclose, generate an autonomous interrupt request based at least in part on the sensor data.
However, Huang teaches, generate an autonomous interrupt request based at least in part on the sensor data (FIG. 1A and ¶[0028] – “…In some examples, a remote operator may be transferred at least partial control of the vehicle or other object in response to a determination (e.g., by the autonomous vehicle or other object) that the vehicle or object cannot or should not (e.g., based on rules, conditions, constraints, etc.) navigate a situation or environment (e.g., debris blocking a safe path, rules of the road prevent the vehicle from proceeding a certain way, a dangerous condition has presented itself, such as a fallen tree or power line, etc.). and ¶[0041] – “…following the constraints of the vehicle 102 (e.g., due to the rules of the road), the vehicle 102 may, without the features and functionality of the present disclosure, remain stopped behind the van 152 until the van 152 moves (or may pass control to a human operator, depending on the embodiment)….”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the autonomous vehicle’s processor as disclosed by Sri to include generating an autonomous request based in part on the sensor data as taught by Huang. Doing so, enhances the control of the vehicle when encountering obstacles without the need of an operator.
Sri discloses autonomous interrupt and a remote operator (¶[0025] – “remote operator”) but does not disclose, transmit the autonomous interrupt request to a remote operator.
However, Huang teaches, transmit the autonomous interrupt request to a remote operator ( ¶[0036] – “…the controls implemented by the remote operator and/or the corresponding sensor data may be applied to machine learning model(s) to train the machine learning models on how to navigate unknown or uncertain situations or scenarios represented by the sensor data. For example, if a vehicle was operating in a previously unexplored location….” and FIG. 6 and ¶[0154 – 0161]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the autonomous vehicle’s processor as disclosed by Sri to include the transmitting of interrupt request as taught by Huang. Doing so, enhances the capability of the processor by providing the interruption request to the remote operator and this improves the capability of the operator to control the vehicle.
Sri discloses the autonomous vehicle and remote operator but does not disclose,
collect, while the autonomous vehicle is controlled using the control signals from the remote operator, additional sensor data.
However, Huang teaches,
collect, while the autonomous vehicle is controlled using the control signals from the remote operator, additional sensor data. (FIG. 6 and ¶[0156] – “The method 600, at block B606, includes receiving sensor data generated by one or more sensors of the vehicle during executing of vehicle controls corresponding to the vehicle control data by the vehicle….”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the autonomous vehicle’s processor as disclosed by modified Sri to include the collecting of control signals request as taught by Huang. Doing so, enhances the capability of the processor by providing the collection of control signals from the remote operator and this improves the capability of the operator to control the vehicle.
Sri discloses the autonomous vehicle model, sensor data, and control signals but does not disclose,
train the autonomous vehicle model by providing the additional sensor data and the control signals to the autonomous vehicle model.
However, Huang teaches,
train the autonomous vehicle model by providing the additional sensor data and the control signals to the autonomous vehicle model (FIGS. 5A-B and ¶[0137] – “Now referring to FIG. 5A, FIG. 5A is an example data flow diagram illustrating a process 500 for training an autonomous vehicle using a machine learning model(s), in accordance with some embodiments of the present disclosure. Any number of inputs, including but not limited to sensor data 502 and/or control data representative of control input(s) to remote control(s) 118 of the remote control system 106, may be input into a machine learning model(s) 504.” and FIG. 6, ¶0154-0161]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the autonomous vehicle’s processor as disclosed by Sri to include the transmitting of interrupt request as taught by Huang. Doing so, enhances the capability of the processor by providing the interruption request to the remote operator and this improves the capability of the operator to control the vehicle.
Regarding Claims 14 and 4, modified Sri further discloses, wherein the remote operator is human controlled (¶[0020] – “…a portion of the vehicle control, with a human user (e.g., a vehicle operator) …”).
Regarding Claims 15 and 5, modified Sri further discloses, wherein the remote operator is an autonomous remote control system (¶[0110] – “…The image or video can provide the remote operator an indication of why the remote-control request was initiated by the autonomous vehicle.”) and
Huang teaches, wherein the remote operator is an autonomous remote control system (¶[0008] – “…a remote operator (e.g., a human, a robot, etc.) may have at least partial control of the vehicle or other object (e.g., a robot, an unmanned aerial vehicle (UAV), etc.), and may provide controls for the vehicle or other object using a remote control system….”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the autonomous vehicle’s processor as disclosed by modified Sri to include the autonomous remote control system as taught by Huang. Doing so, enhances the capability of the processor by providing the interruption request to the remote operator and this improves the capability of the operator to control the vehicle.
Regarding Claims 16 and 6, modified Sri further discloses, wherein the autonomous remote control system comprises a large language model (¶[0143] – “… an object recognition algorithm of the perception system 303 labels objects identified in images captured by one or more of the sensors 301. In some cases, the labeling is incorrect, which can have negative effects on an autonomous vehicle's ability to navigate a route…”).
Regarding Claims 17 and 7, modified Sri further discloses, wherein the autonomous remote control system is trained on rules of a jurisdiction (¶[0061] – “…, map data 326 used by the perception system describes roadways and segments thereof and may also describe: buildings or other items or objects (e.g., lampposts, crosswalks, curbing); location and directions of traffic lanes or lane segments (e.g., the location and direction of a parking lane, a turning lane, a bicycle lane, or other lanes within a particular roadway); traffic control data…”) and
Huang teaches, wherein the autonomous remote control system is trained on rules of a jurisdiction (¶[0066] – “…that two traffic lights in close proximity (e.g., barely offset from one another) are associated with different roads, that in Rhode Island, the first car waiting to make a left turn at traffic light breaks the law by turning before oncoming traffic when the light turns green, and/or other information….”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the autonomous vehicle’s processor as disclosed by modified Sri to include the trained rules of a jurisdiction as taught by Huang. Doing so, enhances the capability by trained rules on a jurisdiction and this improves the capability of the operator to control the vehicle.
Regarding Claims 18 and 8, modified Sri further discloses, wherein the autonomous interrupt request is generated when a confidence value of the autonomous vehicle model is below a threshold value based on the sensor data (¶[0030] – “…compute a confidence value or score for a course of action based on input received from a variety of vehicle sensors. If the score or confidence value meets a criterion (e.g. a highest confidence interval for any possible path forward is below a predetermined threshold confidence level)…”).
Regarding Claims 12 and 2, modified Sri further discloses, wherein the computer-readable instructions further cause the vehicle to classify a driving scenario within a time window surrounding generation the autonomous interrupt request based on the sensor data and the additional sensor data (¶[0027] – “…autonomous vehicles may be routed via route components in which remote operator assistance is planned (e.g., an assisted route component). In some embodiments, a remote operator system tracks motion of the routed autonomous vehicles and queues a remote operator for a vehicle as the vehicle approaches an assisted route component. For example, the remote operator may utilize a remote operator computing device. The remote operator system may feed sensor data …”).
Regarding Claims 13 and 3, modified Sri further discloses, wherein the autonomous vehicle model is further trained by a classified driving scenario (¶[0115] – “Operation 815 detects that a route condition of the route meets a criterion. The route condition may include a weather condition (e.g. rain, snow, fog), road condition (e.g. road surface, markings, signage, obstructions) traffic condition (e.g. accident, congestion), vehicle condition (e.g. mechanical issue), or other condition….”).
Regarding Claims 19 and 9, modified Sri further discloses, wherein the autonomous interrupt request is generated after receiving an assistive driving signal from an assistive driving system (¶[0030] – “… for continued vehicle operation during exceptional circumstances as described above by providing for remote operator assistance to the vehicle when the vehicle encounters a situation that the vehicle's control system is not able to handle independently. Some embodiments may initiate the operator assistance in response to an on-board vehicle control system generating a request for assistance. …”).
Regarding Claims 20 and 10, modified Sri further discloses, wherein the assistive driving system comprises a lane keep assist system, a collision avoidance system, or an adaptive cruise control system (¶[0143] – “…the object recognition algorithm may label,…in some circumstances, the autonomous vehicle to stop or otherwise avoid colliding with the labeled animal….”).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LUIS G DEL VALLE whose telephone number is (303)297-4313. The examiner can normally be reached Monday-Friday, 0730 - 1630 MST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anne Antonucci can be reached at (313) 446-6519. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LUIS G DEL VALLE/Examiner, Art Unit 3666
/ANNE MARIE ANTONUCCI/Supervisory Patent Examiner, Art Unit 3666
