DETAILED ACTION
This office action is in response to the application filed on 08/13/2025. Claim(s) 1-15 is/are pending and are examined.
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 .
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 10/20/2025 and 04/09/2026 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered by the examiner.
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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exception (an abstract idea) without significantly more. The following is Examiner’s analysis of the claimed invention.
Step 1 Is the claim to a process, machine, manufacture, or composition of matter? Claim 1 (and dependent claims 2-7) recites a method (process), Claim 8 (and dependent claims 9-14) and Claim 15 recites a system (machine).
Step 2A Prong one: Does the claim recite an abstract idea, law of nature, or natural phenomenon? The claim recites the steps of, “determining a first prediction data according to a first correction data and a first control data… obtaining a first observation data by the at least one sensor at the moment k; and determining whether the vehicle is under an attack according to the first observation data and the first prediction data.” Which fall with the “Mental Processes” grouping as concepts performed in the human mind (including observation, evaluation, judgment, and opinion). Each of these steps of determining a predication, obtaining first observation data, determining whether a vehicle is under attack is a mental process that can practically be performed in the human mind; therefore, the claimed limitations fall within the mental processes grouping, and the claims recite an abstract idea.
Step 2A Prong two: Does the claim recite additional elements that integrate the judicial exception into a practical application? The claimed preamble, “detecting an attack for a vehicle, wherein the method comprises” generally links the use of the judicial exception to a particular technological environment, therefore the claim as a whole is no more than a drafting effort designed to monopolize the exception.
Step 2B Does the claim recite additional elements that amount to significantly more than the judicial exception? The elements recited in claims 2-7 and 9-14 recite further calculations to be performed on the data, which amounts to no more than mental processes. Claim 15 using a vehicle which amounts to no more than mere instructions to apply the exception using a generic component. See Two-Way Media Ltd. V. Comcast Cable Communications, LLC, 2017 U.S. App. LEXIS 21706 at 14 (Fed. Cir. Nov. 1, 2017) finding “simply implementing an abstract concept on a computer without meaningful limitations to that concept, does not transform a patent-ineligible claim into a patent-eligible one.” Accenture Global Service v. Guidewire Software, Inc., 728 F.3d 1336 (Fed. Cir. 2013) at 1345; see also the prohibition against patenting an abstract principle by attempting to limit the use of the [principle] to a particular technological environment; Classen an example case identifying a mental process. Specifically, “[c]oncepts relating to data comparisons that can be performed mentally or are analogous to human mental work.” See MPEP 2106.04(a)(2), sections III and III A. Therefore, additionally recited limitations individually or in combination as a whole in Claims 1-20 fail to amount to significantly more than the abstract idea.
The dependent claims included in the statement of rejection but not specifically addressed in the body of the rejection have inherited the deficiencies of their parent claim and have not resolved the deficiencies. Therefore, they are rejected based on the same rationale as applied to their parent claims above.
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) 1-5, 8-12, and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Juliato (US 12,141,274 B2), hereinafter Juliato in view of Carter (US 2024/0092397 A1), hereinafter Carter.
Regarding Claim(s) 1, 8, and 15 Juliato teaches:
A method for detecting an attack for a vehicle, wherein the method comprises: (Juliato Abstract, methods and apparatus relating to a physics-based approach for attack detection and/or localization in closed-loop controls for autonomous vehicles are described. Juliato Col. 3 Ln. 17-30 teaches, logic used to generate/calculate the residual signals/pulses/data is mounted or otherwise physically coupled to a vehicle. Juliato Fig. 9)
obtaining a first observation data by the at least one sensor at the moment k; and (Juliato Col. 6 Ln. 42-45 teaches, For attack localization, three residual signals may be generated that indicate how well currently observed speed, current and controller commands correspond to predicted values.)
determining whether the vehicle is under an attack according to the first observation data and the first prediction data. (Juliato Col. 6 Ln. 42-45 teaches, For attack localization, three residual signals may be generated that indicate how well currently observed speed, current and controller commands correspond to predicted values.)
Juliato does not appear to explicitly teach but in related art:
determining a first prediction data according to a first correction data and a first control data, wherein the first correction data is a correction data of at least one sensor in the vehicle at a moment k-1, the first control data comprises at least one control command of the vehicle at the moment k- 1, the first prediction data is a prediction data of the at least one sensor at a moment k, and k is a positive integer; (Carter ¶ 50-51 teaches, The motion model may determine a preliminary predicted state, x.sub.k|k-1. The Kalman filter may receive a prior predicted state {circumflex over (x)}.sub.k-1|k-1, and/or prior uncertainty, P.sub.k-1|k-1. If this if the first time step, no such priors may exist, so an initialization state and/or initialization uncertainty may be used. Such initialization values may be based at least in part on the sensor data received from one or more sensors and/or stored values retrieved by the Kalman filter. (i.e., correction data from sensor). where k is the k-th time tick, F is the state transition model , U is a control/input variable (if any, depending on what system is being monitored, and/or if any is available, which it may not be) (i.e., a measurable (deterministic) input to the system, such as steering commands, applied torque) (i.e., control command))
It would have been obvious to one with ordinary skill the art, prior to the applicant's earliest effective filing date, to combine the teachings of Juliato with Carter, to modify the physics-based approach for attack detection in closed-loop controls for autonomous vehicles with the interpretable Kalman filter for autonomous vehicles of Carter. The motivation to do so Carter ¶ 17, improve the accuracy and/or precision of a Kalman filter, protect accuracy of the Kalman filter from bias or errors in sensor output, and may improve the predictions of the Kalman filter.
Regarding Claim(s) 2 and 9 Juliato in view of Carter teaches:
The method according to claim 1, (Juliato in view of Carter teaches the parent claim above.) wherein the first prediction data is determined based on a Kalman filter. (Carter ¶ 49 teaches, a motion model of a Kalman filter where the motion model includes a state transition model and a neural network to account for unknown and/or undefined control signal(s) for an autonomous vehicle.)
Regarding Claim(s) 3 and 10 Juliato in view of Carter teaches:
The method according to claim 2, wherein the first predication data satisfies a following formula: (Juliato in view of Carter teaches the parent claim above.)
x(k|k -1)= Ax(k -1 k -1)+ Bu(k - 1),
wherein x(klk-1) is the first prediction data, x(k-1|k-1) is the first correction data, u(k-1) is the first control data, and A and B are preset matrices. (Carter ¶ 51 teaches, {circumflex over (x)}.sub.k|k-1=F.sub.k{circumflex over (x)}.sub.k-1|k-1+B.sub.kU.sub.k). where k is the k-th time tick, F is the state transition model 302, U is a control/input variable (if any, depending on what system is being monitored, and/or if any is available, which it may not be) (i.e., a measurable (deterministic) input to the system, such as steering commands, applied torque) B is a control/input transition matrix mapping control to state variables. Juliato Eq. 1 teaches, Ax.sub.k-1+Bu.sub.k-1+w.sub.k-1) )
Regarding Claim(s) 4 and 11 Juliato in view of Carter teaches:
The method according to claim 2, wherein the method further comprises: (Juliato in view of Carter teaches the parent claim above.)
determining a second correction data according to the first prediction data and the first observation data based on the Kalman filter. (Carter ¶ 67 teaches, The Kalman gain neural network comprises a neural network trained to determine a gain (e.g., Kalman gain) based at least in part on the innovation and/or the predicted state.)
Regarding Claim(s) 5 and 12 Juliato in view of Carter teaches:
The method according to claim 4, wherein the second correction data satisfies a following formula: (Juliato in view of Carter teaches the parent claim above.)
x(k k)= x(kIk -1)+K[y(k) -Cx(kIk-1)],
wherein x(klk) is the second correction data, x(klk-1) is the first prediction data, y(k) is the first observation data, and K and C are preset matrices. (Carter ¶ 67 teaches, the Kalman filter may additionally or alternatively determine an observation difference (e.g., Δ{tilde over (y)}.sub.k=y.sub.k−y.sub.k-1) the estimated state may be given by: {circumflex over (x)}.sub.k|k={circumflex over (x)}.sub.k|k-1+K.sub.kΔy.sub.k where K.sub.k is the gain at time step k determined by the Kalman gain neural network.)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20210126943 A1 - A dynamic, resilient estimator may split a temporal monitoring node space into normal and one or more abnormal subspaces associated with different kinds of attack vectors. According to some embodiments, a neutralization model is constructed and trained for each attack vector using supervised learning and the associated abnormal subspace. In other embodiments, a single model is created using out-of-range values for abnormal monitoring nodes.
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/J.B.K./Examiner, Art Unit 2408
/LINGLAN EDWARDS/Supervisory Patent Examiner, Art Unit 2408
