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 Non-Final Office Action, in responses to Patent Application filed 06/25/2024; claims foreign priority to 23181426.0, filed 06/26/2023; claims foreign priority to 24159606.3, filed 02/26/2024. Claim(s) 1-16 are pending. Claim(s) 1, 6, 9 and 14 is/are independent.
In addition, 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 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.
Information Disclosure Statement
A signed and dated copy of applicant’s IDS, which was filed 11/26/2024 is/are attached to this Office Action.
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.
Claim(s) 1-16 fail to recite statutory subject matter, as defined in 35 U.S.C. 101, because: The claimed invention is/are directed to a judicial exception (i.e., abstract idea) without significantly more.
Step 1: YES (Claim(s) is/are process, machine, manufacture or composition of the matter). ... A method for processing sensor data of a vehicle, the method comprising: obtaining sensor data of an environment; applying a segmentation model to the sensor data to obtain segmented sensor data of an object in the environment; and applying a regression model to the segmented sensor data to determine a steering angle for the vehicle ... and therefore, fall into one of the four categories of patent eligible subject matter (process, machine, manufacture or composition of the matter).
Step 2A : Prong One: ( whether a claim recites a judicial exception ?) the claim(s) recite ... A method for processing sensor data of a vehicle, the method comprising: obtaining sensor data of an environment; applying a segmentation model to the sensor data to obtain segmented sensor data of an object in the environment; and applying a regression model to the segmented sensor data to determine a steering angle for the vehicle. …These limitation(s) recite mental processes and mathematical calculation...since train the regression model and segmentation model … [is a high level mathematical calculation(s) ... then [APPLY IT] “applying a segmentation model to the sensor data to obtain segmented sensor data of an object in the environment; and applying a regression model to the segmented sensor data to determine a steering angle for the vehicle”....Thus these limitation(s) recite mental processes and mathematical calculation(s).
--------------Step 2A : Prong Two: (Do the claim(s) recite “additional element(s) that integrate the “Judicial Exception” into “A Practical Application” ? The claim(s) recite additional limitation(s) such as “Apparatus/interface/processing circuit” … for obtaining sensor data of an environment; applying a segmentation model to the sensor data to obtain segmented sensor data of an object in the environment; and applying a regression model to the segmented sensor data to determine a steering angle for the vehicle ...it is noted, the improvement in the abstract idea itself ... but do not integrate the judicial exception into a practical application, i.e., “i.e., steering angle for the vehicle “…Also, these limitation(s) only recite a generic computer component(s) that only amounts to mere instructions to implement the abstract idea on a computer, and therefore, do not integrate the judicial exception into a practical application. (MPEP 2106.04(d), 2106.05(f)).
Step 2B: (Whether a Claim Amounts to Significantly More) ? The claim(s) recite additional limitation(s) such as ... “Apparatus/interface/processing circuit” .. for obtaining sensor data of an environment; applying a segmentation model to the sensor data to obtain segmented sensor data of an object in the environment; and applying a regression model to the segmented sensor data to determine a steering angle for the vehicle .......These limitation(s) only recite a generic computer component(s) that only amounts to mere instructions to implement the abstract idea on a computer, and therefore, do not amount to significantly more than the abstract idea itself (MPEP 2106.05, 2106.04(d) and 2106.05(f)).
As to the dependent claim(s) 2-5, 7-8, 10-13 and 15-16 further recite, addition limitation(s) such as, (lane marking, a lane border, and/or a road, driving assistance system and/or for maneuvering a vehicle, images of the environment, mutual processing pipeline, synthetic and/or real-world data, different environmental circumstances ., etc.,) These limitation(s) only amounts to mere instructions to implement the abstract idea ...and do not include elements that amount to significantly more than the abstract idea and are also rejected under the same rational.
Accordingly, claims 1-16 fail to recite statutory subject matter, as defined in 35 U.S.C. 101.
Claims Rejection – 35 U.S.C. 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 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.
Claim(s) 1-16 rejected under 35 U.S.C. 103 as being unpatentable over Charland et al., (“US-20230373520 A1” filed 02/13/2023 Continuation to 17/840,218 filed 06/14/2022 now US Patent 11,623,658 issued 04/11/2023 [hereinafter “Chaland”], in view of Shalev et al., (“US-20210162993-A1” filed 11/30/2020 [hereinafter “Shalev”].
Independent Claim 1, Charland teaches: A method for processing sensor data of a vehicle, the method comprising: obtaining sensor data of an environment; applying a segmentation model to the sensor data to obtain segmented sensor data of an object in the environment; and applying a regression model to the segmented sensor data to determine a steering angle for the vehicle; (in Charland Para(s) 6, 45-49, 68-69 and 98 , i.e., sensor data of a vehicle, the method comprising: obtaining sensor data of an environment; applying a range view-based model to lidar points to perform or optimize a few tasks, for example, multi-class detection, multi-class trajectory prediction, lidar point segmentation, and class agnostic detection. Moreover, in Para 69 mentions neural network model can be trained using at least one of supervised learning, unsupervised learning, or semi-supervised learning (i.e., In the BRI , the supervised learning model, is in fact the regression learning model as claimed…)
It is noted, Charland discloses method for processing sensor data of a vehicle, the method comprising: obtaining sensor data of an environment; applying a segmentation … to the sensor data to obtain segmented sensor data of an object in the environment; and applying a regression model to the segmented sensor data to determine a steering angle for the vehicle ...(as describes herein) However, Charland does not expressly teach .However, the combination of Charland and Shalev teach the limitation said … obtaining sensor data of an environment; applying a segmentation model to the sensor data to obtain segmented sensor data of an object in the environment; and applying a regression model to the segmented sensor data to determine a steering angle for the vehicle. (in Shave Para(s) 176 -179 and 201 and 325, , i.e., "obtaining sensor data of an environment; applying a segmentation model to the sensor data to obtain segmented sensor data of an object in the environment; and applying a regression model to the segmented sensor data to determine a steering angle for the vehicle…)
Accordingly, it would have been obvious to one having ordinary skill in the art at the time before the effective filing date of the claimed invention was made to modify Charland’s supervised learning, unsupervised learning, or semi-supervised learning (i.e., regression model trained) of” autonomous vehicle”, to include a means said, obtaining sensor data of an environment; applying a segmentation model to the sensor data to obtain segmented sensor data of an object in the environment; and applying a regression model to the segmented sensor data to determine a steering angle for the vehicle as taught by Shalev, provides one or more sensors of an autonomous vehicle (or a vehicle equipped with autonomous vehicle sensors) can capture measurement data and use the measurement data to track and/or identify dynamic objects within the environment surrounding the vehicle. As the state of the objects dynamically changes, improvement in efficiently updating the tracking and/or identifying of the object by using new measurement data remains desired [In Charland Para 20]. It is noted the KSR ruling recommends references directed to similar subject matter to be combined.
Claim 2, Okuno and Ukrainczyk further teach: wherein the object is a lane marking, a lane border, and/or a roads; (in Charland Para(s) 30 and 66 i.e., the road region boundary...stop sign, environment data…)
Claim 3, Okuno and Ukrainczyk further teach: wherein the method further comprises providing the steering angle for a driving assistance system and/or for maneuvering a vehicle; (in Charland Para 45 i.e., steering angle for a driving assistance system …)
Claim 4, Okuno and Ukrainczyk further teach: wherein the sensor data includes one or more images of the environment; (in Charland Para 90-91 i.e., camera images ( vehicle, cyclist, pedestrian, etc., or known obscurants such as rain, snow, fog, smoke, exhaust, etc.) …)
Claim 5, Okuno and Ukrainczyk further teach: wherein the segmentation model and the regression model are implemented in a mutual processing pipeline; (in Charland Para(s) 124-125 i.e., models sharing a GPU may need to be running at comparable cadence. In some implementations, two models sharing a GPU can run at comparable cadence by both models performing batch processing at a predetermined frequency, or both models performing stream processing with a shared process queue (i.e., mutual processing pipeline) …)
Regarding Claim 6 is/are fully incorporated similar subject of claim 1 cited above.
Claim 7, Okuno and Ukrainczyk further teach: wherein the sensor data includes synthetic and/or real-world data; (in Charland Para 90-91 i.e., camera images ( vehicle, cyclist, pedestrian, etc., or known obscurants such as rain, snow, fog, smoke, exhaust,(real word data etc.) …)
Claim 8, Okuno and Ukrainczyk further teach: wherein the sensor data is indicative of different environmental circumstances; (in Charland Para 90-91 i.e., camera images ( vehicle, cyclist, pedestrian, etc., or known obscurants such as rain, snow, fog, smoke, exhaust, …)
Regarding Claim(s) 9-13 (respectively) is/are fully incorporated similar subject of claim(s) 1-5 (respectively) cited above.
Regarding Claim(s) 14-16 (respectively) is/are fully incorporated similar subject of claim(s) 6-8 (respectively) cited above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Saleem et al., NPL (“Steering Angle Prediction Techniques for Autonomous Ground Vehicles: A Review” Published 2021 by IEEE, 19 pages, describing, Unintentional lane departure accidents are one of the biggest reasons for the causalities that occur due to human errors. By incorporating lane-keeping features in vehicles, many accidents can be avoided. The lane-keeping system operates by auto-steering the vehicle in order to keep it within the desired lane, despite of changes in road conditions and other interferences. Accurate steering angle prediction is crucial to keep the vehicle within the road boundaries, which is a challenging task. The main difficulty in this regard is to identify the drivable road area on heterogeneous road types varying in color, texture, illumination conditions, and lane marking types. This strenuous problem can be addressed by two approaches, namely, `computer-vision-based approach' and `imitation-learning-based approach'. To the best of our knowledge,
at present, there is no such detailed review study covering both the approaches and their related optimization techniques. This comprehensive review attempts to provide a clear picture of both approaches of steering angle prediction in the form of step by step procedures. The taxonomy of steering angle prediction has been presented in the paper for a better comprehension of the problem. We have also discussed open research problems at the end of the paper to help the researchers of this area to discover new research horizons.. [The Abstract].
Any inquiry concerning this communication or earlier communications from the examiner should be directed to QUOC A TRAN whose telephone number is (571)272-8664. The examiner can normally be reached Monday-Friday 9am-5pm EST.
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, Cesar Paula can be reached at 571-272-4128. 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.
/QUOC A TRAN/Primary Examiner, Art Unit 2145
