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
Applicant’s arguments, see pages 6-8, filed 06/12/2025, with respect to the rejection under 35 USC 112(a) and the potential reinstatement of the rejection under 35 USC 101 have been fully considered and are persuasive. The rejection under 35 USC 112(b) of 03/14/2025 has been withdrawn.
Applicant’s arguments, see page 9, filed 06/12/2025, with respect to the teachings of Kottke et al. have been fully considered and are persuasive. Therefore, the associated rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Agarwal et al. (US 20220198842), as discussed in detail below.
Applicant's arguments filed 06/12/2025 with regard to the combination of Schoenfeld et al. and Mahipal et al. have been fully considered but they are not persuasive. The applicant argues that the two references are nonanalogous because Schoenfeld et al. is directed toward a factory testing system whereas Mahipal et al. is directed toward a component health classifier. However, both Schoenfeld et al. and Mahipal et al. are concerned with detecting the presence of a fault in a vehicle, with Schoenfeld et al. teaching a general operability and Mahipal et al. teaching locating a fault in a particular component. A person of ordinary skill in the art would have recognized that these two actions could be performed in succession (i.e., the presence of a general fault is detected, and then is further localized to a particular component using a classifier) with predictable results.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: "control unit" in claims 1, 2, and 13.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. Specifically, the “control unit” is being interpreted as processing circuitry which executes instructions stored in a memory (see Fig. 4 and pg. 11, lines 9-16).
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
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.
Claim(s) 1, 3-6, 10, 11, 14, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Schoenfeld et al. (US 20180052456, cited in previous action) in view of Agarwal et al. (US 20220198842), and further in view of Mahipal et al. (US 20220067667, cited in previous action).
Claim 1.
Schoenfeld et al. teaches:
a control unit and a remote sensor arranged separate from the autonomous vehicle, where the control unit is arranged to
(Schoenfeld – [0026]) “System 200 comprises one or more scanning devices 205 for scanning a behavior of motor vehicle 100 as well as a processing device 210.”
control the autonomous vehicle to perform a test case maneuver in the test area
(Schoenfeld – [0028]) “A request for the performance is then transmitted via wireless interfaces 220, 145 to motor vehicle 100. … If motor vehicle 100 obeys and performs the requested maneuver autonomously, then scanning device 205 is able to scan its behavior and compare it to the predetermined behavior from data memory 215”
obtain, via the remote sensor, a current driving behavior of the autonomous vehicle in a test area
(Schoenfeld – [0026]) “System 200 comprises one or more scanning devices 205 for scanning a behavior of motor vehicle 100 as well as a processing device 210.”
obtain a baseline driving behavior of the autonomous vehicle in the test area
(Schoenfeld – [0028]) “If motor vehicle 100 obeys and performs the requested maneuver autonomously, then scanning device 205 is able to scan its behavior and compare it to the predetermined behavior from data memory 215.”
determine a deviation of the current driving behavior from the baseline driving behavior
(Schoenfeld – [0028]) “If a deviation is ascertained in the process that lies above a tolerable magnitude, then it is possible to rate motor vehicle 100 as not fully functional.”
determine a fault in the vehicle based on the determined deviation
(Schoenfeld – [0028]) “If a deviation is ascertained in the process that lies above a tolerable magnitude, then it is possible to rate motor vehicle 100 as not fully functional.”
detect a maintenance need of the autonomous vehicle based on the determined deviation
(Schoenfeld – [0028]) “If a deviation is ascertained in the process that lies above a tolerable magnitude, then it is possible to rate motor vehicle 100 as not fully functional.”
if the determined deviation is above a predetermined threshold, recommend maintenance
(Schoenfeld – [0028]) “If a deviation is ascertained in the process that lies above a tolerable magnitude, then it is possible to rate motor vehicle 100 as not fully functional.”
[Examiner Note: A rating of “not fully functional” would indicate to a person of ordinary skill in the art that maintenance would be recommended to restore full functionality.]
determine a maintenance need based on the determined deviation of the current driving behavior from the baseline driving behavior
(Schoenfeld – [0028]) “A request for the performance is then transmitted via wireless interfaces 220, 145 to motor vehicle 100. … If motor vehicle 100 obeys and performs the requested maneuver autonomously, then scanning device 205 is able to scan its behavior and compare it to the predetermined behavior from data memory 215”
(Schoenfeld – [0028]) “If a deviation is ascertained in the process that lies above a tolerable magnitude, then it is possible to rate motor vehicle 100 as not fully functional.”
[Examiner Note: A rating of “not fully functional” would indicate to a person of ordinary skill in the art that maintenance would be recommended to restore full functionality.]
wherein the current driving behavior and baseline driving behavior comprise one or more locations or travelled path of the autonomous vehicle in the test area and any of velocity, acceleration, yaw, and yaw rate of the autonomous vehicle
(Schoenfeld – [0009]) “It is particularly preferred that the behavior comprises a driver-independent longitudinal and/or lateral control of the motor vehicle. Additionally, it is possible to perform autonomously for example a navigation, an obstacle avoidance or a tactical or strategic planning of a trajectory to be followed.”
[Examiner Note: Velocity, acceleration, yaw, and yaw rate are inherent to an obstacle avoidance (the route will have at least a speed, a stopping point, and a turn).]
Schoenfeld et al. does not explicitly teach how a baseline driving behavior is determined; however, Agarwal et al. teaches:
wherein the baseline driving behavior is obtained from one or more previous observations of the autonomous vehicle in the test area
(Agarwal – [0114]) “The predictive maintenance module 1402 uses data associated with the detected events in conjunction with historical data for the vehicle or other vehicles (or both) to determine the health of some or all of the components of the vehicle.”
It would have been obvious to one possessing ordinary skill in the art before the effective filing date to combine these teachings, modifying the “predetermined behavior” of Schoenfeld et al. such that it corresponds to the historical data of Agarwal et al. Both of these references are concerned with the determination of mechanical faults using sensor data; therefore a person of ordinary skill in the art would have recognized that data obtained by the remotely located sensors of Schoenfeld et al. could be compared to historical data previously obtained by said sensors in a similar fashion to that of Agarwal et al. One would have been motivated to do this in order to make a measurement of the actual wear and tear experienced by the components of the vehicle (Agarwal – [0113]).
Neither Schoenfeld et al. nor Agarwal et al. explicitly teaches recommending maintenance based on a computer-implemented classification model; however, Mahipal et al. teaches:
wherein the maintenance is recommended based on a computer-implemented classification model arranged to determine a maintenance need based on the determined deviation
(Mahipal – [Abstract]) “The received operational data is used as an input to the trained classifier and the health status of the component is determined based on an output of the trained classifier.”
It would have been obvious to one possessing ordinary skill in the art before the effective filing date to combine these teachings, modifying the vehicle test system of Schoenfeld et al. with the predictive maintenance system of Mahipal et al. One would have been motivated to do this in order to diagnose the occurrence of faults in a plurality of specific components (Mahipal – [0027]).
Claim 3.
The combination of Schoenfeld et al., Agarwal et al., and Mahipal et al. teaches all the limitations of claim 1, as discussed above. Schoenfeld et al. does not explicitly teach a specific system fault; however, Agarwal et al. teaches:
wherein the fault comprises any of tire wear, mechanical defects, suspension system failure, and brake failure
(Agarwal – [0031]) “A vehicle (such as an autonomous vehicle) utilizes onboard sensors to collect data about events that affect the operational health of electrical and mechanical components of the vehicle.”
(Agarwal – [0054]) “the AV system 120 includes devices 101 that are instrumented to receive and act on operational commands from the computer processors 146. … Examples of devices 101 include a steering control 102, brakes 103, gears, accelerator pedal or other acceleration control mechanisms, windshield wipers, side-door locks, window controls, and turn-indicators.”
It would have been obvious to one possessing ordinary skill in the art before the effective filing date to combine these teachings, modifying the vehicle test system of Schoenfeld et al. such that it detects faults in the systems disclosed by Agarwal et al. A person of ordinary skill in the art would have recognized that the general operability of Schoenfeld et al. could be modified with the operability of particular components with predictable results. One would have been motivated to do this because specificity in which component is faulty allows for more effective maintenance.
Claim 4.
The combination of Schoenfeld et al., Agarwal et al., and Mahipal et al. teaches all the limitations of claim 1, as discussed above. Schoenfeld et al. further teaches:
wherein the remote sensor comprises any of a camera, an IR camera, a lidar, a sonar, a microphone, and a radar
(Schoenfeld – [0010]) “The scanning device may comprise for example an ultrasonic sensor, a camera, a video camera, a radar or lidar device.”
Claim 10.
The combination of Schoenfeld et al., Agarwal et al., and Mahipal et al. teaches all the limitations of claim 1, as discussed above. Mahipal et al. further teaches:
wherein the computer-implemented classification model is based on any of a neural network, a random forest structure, a support vector machine model, a logistic regression algorithm, a Bayes algorithm, a decision tree algorithm, and a K-nearest neighbors algorithm
(Mahipal – [0048]) “Examples of the classifier 210 may include but are not limited to, a Support Vector Machine (SVM), a Logistic Regression, a Bayesian Classifier, a Decision Tree Classifier, a Copula-based Classifier, a K-Nearest Neighbors (KNN) Classifier, or a Random Forest (RF) Classifier, a deep learning based classifier, or a neural network based classifier.”
It would have been obvious to one possessing ordinary skill in the art before the effective filing date to combine these teachings for the reasons given in discussion of claim 1.
Claim 11.
The combination of Schoenfeld et al., Agarwal et al., and Mahipal et al. teaches all the limitations of claim 1, as discussed above. Schoenfeld et al. does not explicitly teach how the baseline driving behavior is determined; however, Agarwal et al. teaches:
wherein the baseline behavior is obtained from one or more previous observations of one or more autonomous vehicles in the test area
(Agarwal – [0114]) “The predictive maintenance module 1402 uses data associated with the detected events in conjunction with historical data for the vehicle or other vehicles (or both) to determine the health of some or all of the components of the vehicle.”
It would have been obvious to one possessing ordinary skill in the art before the effective filing date to combine these teachings for the reasons given in discussion of claim 1.
Claim 14.
Rejected by the same rationale as claim 1.
Claim 16
The combination of Schoenfeld et al., Agarwal et al., and Mahipal et al. teaches all the limitations of claim 1, as discussed above. Mahipal et al. further teaches:
returning a diagnostic based on the deviation to the
(Mahipal – [0027-0028]) “The OBD device 120 may be further configured to generate one or more diagnostic trouble codes (DTCs) based on the diagnosis of the plurality of components. … The telematics device 104 may further obtain the operational data in the form of DTCs from the OBD device 120.”
[Examiner’s Note: While Mahipal et al. does not explicitly teach an autonomous vehicle, such a vehicle is taught by Schoenfeld et al., as discussed above.]
Claim 1 is alternatively rejected under and claims 9 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Schoenfeld et al. and Agarwal et al., and further in view of Saleh et al. (WO 2022223434, cited in previous action).
Claim 1. (alternate)
The combination of Schoenfeld et al. and Agarwal et al. teaches all the limitations of claim 1 except for the computer-implemented classification model, as discussed above. Neither Schoenfeld et al. nor Agarwal et al. explicitly teaches a classification model; however, Saleh et al. teaches:
wherein maintenance is recommended based on a computer-implemented classification model arranged to determine a maintenance need based on the determined deviation
(Saleh – Abstract) “generating a set of scenarios to verify the behavior of the autonomous vehicle … wherein the set of scenarios comprise parameters associated driving conditions of the autonomous vehicle”
It would have been obvious to one possessing ordinary skill in the art before the effective filing date to combine these teachings, modifying the vehicle test system of Schoenfeld et al. with the set of scenarios of Saleh et al. One would have been motivated to do this because it allows for verification against “unknown unsafe scenarios that cannot be directly designed for or verified” (Saleh – Page 1, line 36 to Page 2, line 2).
Claim 9.
The combination of Schoenfeld et al., Agarwal et al., and Saleh et al. teaches all the limitations of claim 1 (alternate), as discussed above. Saleh et al. further teaches:
wherein the computer-implemented classification model is based on any of a look up table and an analytical function
(Saleh – Abstract) “generating a set of scenarios to verify the behavior of the autonomous vehicle … wherein the set of scenarios comprise parameters associated driving conditions of the autonomous vehicle”
[Examiner Note: The set of scenarios, which comprise sets of parameters associated with safe and unsafe driving scenarios corresponds to the claimed look-up table.]
It would have been obvious to one possessing ordinary skill in the art before the effective filing date to combine these teachings for the same reasons given in discussion of claim 1 (alternate).
Claim 12.
The combination of Schoenfeld et al. and Agarwal et al. teaches all the limitations of claim 1 (alternate), as discussed above. Schoenfeld et al. does not explicitly teach how baseline driving behavior is determined; however, Saleh et al. teaches:
wherein the baseline driving behavior is obtained from a planned driving behavior of the autonomous vehicle
(Saleh – [Abstract]) “generating a set of scenarios to verify the behavior of the autonomous vehicle … wherein the set of scenarios comprise parameters associated driving conditions of the autonomous vehicle”
(Saleh – Page 13, line 37 to Page 14, line 3) “The autonomous vehicle 312, the target vehicle 314 and the road layout 316 are described in a scenario description language that dictate that expected behavior.”
It would have been obvious to one possessing ordinary skill in the art before the effective filing date to combine these teachings for the same reasons given in discussion of claim 1 (alternate).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SARAH A MUELLER whose telephone number is (703)756-4722. The examiner can normally be reached M-Th 7:30-12:00, 1:00-5:30; F 8:00-12:00.
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/S.A.M./Examiner, Art Unit 3669
/NAVID Z. MEHDIZADEH/Supervisory Patent Examiner, Art Unit 3669