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. DETAILED ACTION This is a non - final office action on the merits. Claims 1-20 are pending. Claim Rejections - 35 USC § 101 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-20 are rejected under 35 U.S.C. 101 because they are directed to an abstract idea without more. Claims 1-20 are directed to a system, method and non transitory medium respectively. (claims 1, 10 and 19) Yes the claims are directed to statutory classes. Claim 10 will be analyzed. The limitations under there broadest reasonable interpretation cover the performance of the limitation as mental processes, that could be performed in the human mind. Here the elements of claim 10 include, receiving a first output generated by a … ; receiving one or more confidence levels of the first output generated by the … ; processing, at … , the first output of the … and the one or more confidence levels of the first output of the … as an input of … ; and generating a second output of … based on the processing of the first output of … and the one or more confidence levels of the first output of … . Here aside from first and second machine learning models, the claims are devoid of any hardware or any element at all. Basically using a machine learning model (2). Thus the claims are just using generic limitations to modify an abstract idea. Claims 2, 4 contain the term processor. Step 2a prong 1 yes the claims recite an abstract idea. There is no practical application and no additional elements in the claims. Just an abstract idea and some generic elements. Step 2A prong 2 no there is not practical application. The steps are essentially drafted as a solution without any steps to perform the solution MNPEP 2106.05f1. Step 2B no the claims do not provide significantly more. The dependent claims 2-9 and 11-18,20 are rejected because they do not further improve the abstract idea of claims 1, 10 and 19. The examiner notes that the specification itself may have sufficient material within it to create a inventive concept based on autonomous vehicle navigation. 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. Claim (s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Publication to Xu , 20220326023 As per claim 10 Xu discloses A method comprising: receiving a first output generated by a first machine learning (ML) model; receiving one or more confidence levels of the first output generated by the first ML model; processing, at a second ML model, the first output of the first ML model and the one or more confidence levels of the first output of the first ML model as an input of the second ML model; Xu( 0042) and generating a second output of the second ML model based on the processing of the first output of the first ML model and the one or more confidence levels of the first output of the first ML model. (0056, multiple models and in some cases the model data may be compared or used as inputs to the various models) Claims 19 and 1 are similar to claim 10. As per claim 2 . The system of claim 1, wherein the one or more processors are configured to: provide the second output of the second ML model to a computing system associated with an autonomous vehicle. Xu( 0015, autonomous vehicle) Claims 11 and 20 are similar to claim 2. As per claim 3, Xu discloses; The system of claim 1, wherein processing the first output of the first ML model and the one or more confidence levels of the first output of the first ML model comprises: processing at least a portion of the first output of the first ML model based on the one or more confidence levels of the first output of the first ML model, wherein a respective confidence level from the one or more confidence levels corresponding to at least the portion of the first output of the first ML model exceeds a confidence threshold. Xu( 0028 confidence values, 0033, threshold) Claim 12 is similar to claim 3 As per claim 4, Xu discloses; The system of claim 1, wherein the one or more processors are configured to: train the second ML model with the second output of the second ML model, wherein training of the second ML model is independent of training of the first ML model. Xu( 0097-98 multiple models …. ) Claim 13 is similar to claim 4. As per claim 5 Xu discloses; t he system of claim 1, wherein the first ML model is trained with the first output of the first ML model and the one or more confidence levels of the first output, wherein training of the first ML model is independent of training of the second ML model. Xu( 0106) Claim 14 is similar to claim 5. As per claim 6, Xu discloses; The system of claim 1, wherein the one or more confidence levels of the first output generated by the first ML model comprises a margin of error of the first output generated by the first ML model. Xu( 0108 and 0113) Claim 15 is similar to claim 6. As per claim 7 Xu discloses; The system of claim 1, wherein the first output of the first ML model includes a depth map comprising a depth value for each pixel of the depth map and the one or more confidence levels of the first output generated by the first ML model includes an error map indicative of a respective error for each pixel of the depth map. Xu( 0108, 0115, pixel confidence) Claim 16 is similar to claim 7. As per claim 8 Xu discloses; The system of claim 7, wherein the second output of the second ML model is based on one or more pixels of the depth map that have the respective error that is below an error threshold. Claim 17 is similar to claim 8. As per claim 9 Xu discloses; The system of claim 1, wherein the first ML model includes at least one of an object detection model and an object classification model. Xu( 0028, 0079 one of could be one, but definitely classification and detection) Claim 18 is similar to claim 9. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Handling Occlusions in Automated Driving Using a Multiaccess Edge Computing Server-Based Environment Model From Infrastructure Sensors, (Year: 2021) Uncertainty-Aware Driver Trajectory Prediction at Urban Intersections, IEEE (Year: 2019) Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT BRUCE I EBERSMAN whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)270-3442 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT 8:00 am - 5:00 pm Monday-Friday . 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. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /BRUCE I EBERSMAN/ Primary Examiner, Art Unit 3693