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 .
Status of the Claims
This Office Action is in response to the Applicant’s amendments and remarks filed May 11, 2026. Claims 1, 8, 10, 11, and 18 have been amended. Claims 6, 9, 16, have been canceled. Claims 1-5, 7, 8, 10-15, and 17-19 are pending.
Response to Remarks/Arguments
Applicant’s arguments and amendments filed May 11, 2026 with respect to the previous 35 U.S.C. 101 rejections have been fully considered and rendered moot by the present amendments.
Applicant’s arguments and amendments filed May 11, 2026 with respect to the previous 35 U.S.C. 102 and 103 rejections have been fully considered are persuasive. Therefore, the rejections have been withdrawn. However, upon further consideration, a new ground(s) of rejection is made as discussed below.
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.
Claims 1-3, 5, 10-13, 15, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Srinivasan et al., US 20200239023 A1, in view of El Assaad, US 20240124033 A1, hereinafter referred to as Srinivasan, and El Assaad, respectively.
As to claim 1, Srinivasan discloses a method for route building, comprising:
determining an autonomous driving availability for a route based on at least one performance metric for a plurality of locations along the route, wherein the autonomous driving availability is availability of a vehicle to operate based on instructions from at least one system of the vehicle (Routing according to autonomous vehicle metrics – See at least ¶23);
determining a remote driving availability for the route based on expected network conditions at the plurality of locations along the route, wherein the remote driving availability is availability of the vehicle to operate based on instructions from a remote system which is remote from the vehicle (Routing according to remote operator assistance – See at least ¶27);
optimizing the route by applying a weighted graph algorithm to a plurality of values representing at least the autonomous driving availability and the remote driving availability along the route, wherein the route is optimized such that at least a first portion of the route is navigated using autonomous driving and at least a second portion of the route is navigated using remote driving (Lowest cost, i.e., optimization – See at least ¶22; Cost indicative of traversal between route components – See at least ¶22; Routing graph and constraint data used to generate route – See at least ¶80; Costs may be changed up or down based on costs to traverse favored/disfavored route segments – See at least ¶84; Routing cost may indicative of requirement of remote operation versus autonomous control – See at least ¶93); and
executing the optimized route by causing the vehicle to navigate using autonomous driving during at least the first portion of the route and to navigate using remote driving during at least the second portion of the route (Execute selected route – See at least ¶111 and Fig. 7; Examiner notes selected routes may consist of first portions in which autonomous driving is performed, and second portions in which remote driving is performed.).
Srinivasan fails to explicitly disclose determining a remote driving availability for the route based on expected network conditions at the plurality of locations along the route and determining the remote driving availability further comprises inputting at least one first location of the plurality of locations along the route to a machine learning model, wherein the machine learning model is trained to estimate remote driving availability, wherein the machine learning model is trained based on a training data set including historical network condition data for each of the at least one first location.
However, 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 invention of Srinivasan and include the feature of determining a remote driving availability for the route based on expected network conditions at the plurality of locations along the route and determining the remote driving availability further comprises inputting at least one first location of the plurality of locations along the route to a machine learning model, wherein the machine learning model is trained to estimate remote driving availability, wherein the machine learning model is trained based on a training data set including historical network condition data for each of the at least one first location, with a reasonable expectation of success, because Srinivasan discloses using machine learning models to predict various goals of vehicle control and motion planning (See at least ¶67 of Srinivisan) and El Assaad teaches it is well-known and routine to predict quality of service when determining when and where remote driving is available during operation of a vehicle along a route, wherein in the prediction is based on a history of quality of service (See at least Abstract and ¶51 of El Assaad). Examiner notes applying Srinivisan’s disclosure of machine learning to incorporate predicting the QoS of El Assaad involves routine skill in the art given the ubiquity and commonplace use of machine learning in vehicle control and motion planning.
Independent claims 10 and 11 are rejected under the same rationale as claim 1 because the claims recite nearly identical subject matter but for minor differences due to the claims being directed to different stator categories of invention.
As to claims 2, and 12, Srinivasan discloses determining an amount of remote driving service available to a user of the vehicle during a time period in which the route is to be navigated, wherein the route is optimized based further on the amount of remote driving service (Lowest cost, i.e., optimization – See at least ¶22; Cost indicative of traversal between route components – See at least ¶22; Routing cost may indicative of requirement of remote operation versus autonomous control – See at least ¶93; Examiner notes a plurality of route segments where remote driving occurs is analogous to the claimed “amount.”).
As to claims 3, and 13, Srinivasan discloses identifying at least one service provider location with respect to the route based on a service request, wherein the route is optimized based further on the at least one service provider location (Route segment/component locations – See at least ¶36; Remote assistance locations – See at least ¶40).
As to claims 5, and 15, Srinivasan discloses determining the autonomous driving availability by inputting at least one first location of the plurality of locations along the route to a machine learning model, wherein the machine learning model is trained to estimate autonomous driving availability at a location of each of the at least one first location, wherein the machine learning model is trained based on a training data set including at least one driving performance metric measured at the location of each of the at least one first location (Prediction system including machine-learned models – See at least ¶67).
As to claim 19, Srinivasan discloses executing the optimized route by causing the vehicle to navigate using autonomous driving during at least the first portion of the route and to navigate using remote driving during at least the second portion of the route (Execute selected route – See at least ¶111 and Fig. 7).
Claims 4, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Srinivasan et al., US 20200239023 A1, in view of El Assaad, US 20240124033 A1, as applied above to claim 1, and further in view of Allen et al., US 20250074454 A1, hereinafter referred to as Srinivasan, El Assaad, and Allen, respectively.
As to claims 4, and 14, the combination of Srinivasan and El Assaad fails to explicitly disclose training a machine learning model to learn user preferences of a user with respect to remote driving, wherein the machine learning model is trained using a training data set including previous usage of remote driving capabilities by the user, wherein the route is optimized based further on the learned user preferences. However, 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 combination of Srinivasan and El Assaad and include the feature of training a machine learning model to learn user preferences of a user with respect to remote driving, wherein the machine learning model is trained using a training data set including previous usage of remote driving capabilities by the user, wherein the route is optimized based further on the learned user preferences, with a reasonable expectation of success, because Allen teaches it is well-known and routine in the autonomous vehicle arts to train a model that reflects user preferences (See at least Abstract of Allen).
Claims 7, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Srinivasan et al., US 20200239023 A1, in view of El Assaad, US 20240124033 A1, as applied above to claim 1, and further in view of Anabuki et al., US 20220234625 A1, hereinafter referred to as Srinivasan, El Assaad, and Anabuki, respectively.
As to claims 7, and 17, Srinivasan discloses the optimized route is a first optimized route for a first route among a plurality of routes, further comprising: determining an autonomous driving availability and a remote driving availability for each of the plurality of routes; and optimizing each of the plurality of routes in order to create a plurality of optimized routes including the first optimized route (System generates routes – See at least ¶21 and ¶60; Examiner notes Srinivasan’s use of plural routes is indicative of a plurality of routes similar to the claim language.).
The combination of Srinivasan and El Assaad fails to explicitly disclose presenting each of the plurality of optimized routes to a user. However, 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 combination of Srinivasan and Assaad and include the feature of presenting each of the plurality of optimized routes to a user, with a reasonable expectation of success, because Anabuki teaches it is well-known and routine in the vehicle navigation arts to present a user with a plurality of routes (See at least ¶54 of Anabuki).
Claims 8, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Srinivasan et al., US 20200239023 A1, in view of El Assaad, US 20240124033 A1, as applied above to claims 7, and 17, and further in view of Slusar, US 11466999 B1, hereinafter referred to as Srinivasan, El Assaad, and Slusar, respectively.
As to claims 8, and 18, the combination of Srinivasan and El Assaad fails to explicitly disclose receiving a selection of one of the plurality of optimized routes from the user, wherein the executed route is the selected route. However, 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 combination of Srinivasan and El Assaad and include the feature of receiving a selection of one of the plurality of optimized routes from the user, wherein the executed route is the selected route, with a reasonable expectation of success, because Slusar teaches it is well-known and routine in the autonomous vehicle control arts to control a vehicle according to a user-selected route (See at least Claim 1 of Slusar).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Lail Kleinman whose telephone number is (571)272-6286. The examiner can normally be reached M-F 8:00-5:00.
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, Fadey Jabr can be reached at (571)272-1516. 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.
/LAIL A KLEINMAN/Primary Examiner, Art Unit 3668
Prosecution Insights