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 Claims
Claims 1-20 of U.S. Application No. 18/938,704 filed on 11/06/2024 have been examined.
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 the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claimed invention is directed to the concept of tracking and predicting the movement of unclassified object in a scene. This judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The Examiner will further explain in view of the Revised Patent Subject Matter Eligibility Guidance:
Claims 1, 9 and 17 are directed to a system of controlling a vehicle (i.e., an apparatus). Therefore, Claims 1, 9 and 17 is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claims 1, 9 and 17 include limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection.
Claims 1, 9 and 17, recites: An ego vehicle comprising:
at least one processor;
and a storage medium storing computer-readable instructions that, when executed by the at least one processor, enable the at least one processor to:
obtain, via at least one sensor, first driving state information of a front vehicle traveling before the ego vehicle;
determine a required torque of the ego vehicle based on the first driving state information of the front vehicle and second driving state information of the ego vehicle;
generate a virtual accelerator pedal sensor (APS) map for the ego vehicle based on the required torque and the second driving state information of the ego vehicle;
determine revolutions per minute (RPM) and a gear stage of the ego vehicle based on the virtual APS map;
determine a final gear stage of the ego vehicle based on a predicted gear stage and a preset gear stage;
and in response to the determined final gear stage being out of a preset reference gear range, redetermine the final gear stage based on a shift pattern map.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “obtain, generate, determine …” in the context of this claim encompasses a person looking at data collected and forming a simple judgement. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
An ego vehicle comprising:
at least one processor;
and a storage medium storing computer-readable instructions that, when executed by the at least one processor, enable the at least one processor to:
obtain, via at least one sensor, first driving state information of a front vehicle traveling before the ego vehicle;
determine a required torque of the ego vehicle based on the first driving state information of the front vehicle and second driving state information of the ego vehicle;
generate a virtual accelerator pedal sensor (APS) map for the ego vehicle based on the required torque and the second driving state information of the ego vehicle;
determine revolutions per minute (RPM) and a gear stage of the ego vehicle based on the virtual APS map;
determine a final gear stage of the ego vehicle based on a predicted gear stage and a preset gear stage;
and in response to the determined final gear stage being out of a preset reference gear range, redetermine the final gear stage based on a shift pattern map.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of “storage medium, Processor” the examiner submits that these limitations are an attempt to generally link additional elements to a technological environment. In particular, the determining, obtaining, generating by a processor is recited at a high level of generality and merely automates the determining steps, therefore acting as a generic computer to perform the abstract idea. The processor is claimed generically and is operating in its ordinary capacity and does not use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. The additional limitation is no more than mere instructions to apply the exception using a computer processor.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the Revised Guidance, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “storage medium, processor” amounts to nothing more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Hence, the claim is not patent eligible.
Dependent claims 2-8, 10-16 and 18-20 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 2-8, 10-16 and 18-20 are not patent eligible under the same rationale as provided for in the rejection of Claims 1, 9 and 17.
Therefore, claims 1-20 are ineligible under 35 USC §101.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Debeauwais et al. (US20230234585A1) in view of Ng et al. (US20240059285A1), hereinafter referred to as Debeauwais and Ng respectively.
Regarding claims 1, 9 and 17, Debeauwais discloses A method of controlling an ego vehicle comprising at least one processor, the method comprising:
obtaining, by control of the at least one processor, first driving state information of a front vehicle traveling before the ego vehicle via at least one sensor of the ego vehicle Debeauwais discloses this limitation. See [0009]: “An adaptive cruise control system of this kind comprises a means for detecting the environment of the vehicle such as a radar and is therefore able to detect other vehicles or objects on the carriageway, and is in particular able to detect a vehicle that is preceding it on the carriageway.” See also [0052]: “In this driver assistance system according to the invention are found sensors C such as perception sensors enabling measurement of the dynamics of the ego but also perception of the environment, these sensors C here make it possible not only to provide information as to the speed and the acceleration of the ego, as well as the position, speed and acceleration of the objects in the environment, but also to supply the trajectory prediction for those objects.”
determining, by control of the at least one processor, a required torque for the ego vehicle based on the first driving state information of the front vehicle and second driving state information of the ego vehicle. Debeauwais discloses this limitation. See Abstract: “determining a virtual barycentric target, including calculating a position of the virtual barycentric target, a speed of the virtual barycentric target, and an acceleration of the virtual barycentric target; calculating a longitudinal speed setpoint of the ego vehicle, an acceleration setpoint, and a torque setpoint.” See also [0020]: “the torque of the actuators A (engine, brakes, etc.) being controlled by the torque setpoint Cc generated at the output of the torque control unit CC.”
generating, by control of the at least one processor, the ego vehicle based on the required torque and the second driving state information of the ego vehicle. Debeauwais discloses this limitation. See Abstract: “determining a virtual barycentric target, including calculating a position of the virtual barycentric target, a speed of the virtual barycentric target, and an acceleration of the virtual barycentric target; calculating a longitudinal speed setpoint of the ego vehicle, an acceleration setpoint, and a torque setpoint.” See also [0020]: “the torque of the actuators A (engine, brakes, etc.) being controlled by the torque setpoint Cc generated at the output of the torque control unit CC.”
generating, by control of the at least one processor, a virtual accelerator pedal sensor (APS) map for the ego vehicle based on the required torque and the second driving state information of the ego vehicle. Debeauwais discloses generating virtual setpoints (analogous to virtual APS mapping) based on torque and ego states. See Abstract and CLM 14: “said longitudinal speed setpoint being a function of the position of the virtual barycentric target, the speed of the virtual barycentric target, and the acceleration of the virtual barycentric target.”
determining, by control of the at least one processor, a final gear stage of the ego vehicle based on the determined gear stage and a preset gear stage. Debeauwais discloses determining final setpoints based on virtual vs. driver presets. See [0019]: “The kinematic attributes Att_Target of the identified target are then transmitted to the distance control unit CD that also takes as input driver data DC that is the control speed selected by the driver and the predetermined following time chosen by the driver, by default 2 seconds, which is also translated into a predetermined following distance setpoint chosen by the driver as a function of the speed of the ego vehicle, for example by means of a table.” Ng teaches comparison/selection. See [0073]: “the prelimit selector 126 may generate the subset 128 by removing one or more of the speed profile(s) 124 based on one or more factors.”
Debeauwais does not explicitly teach in response to the determined final gear stage being out of a preset reference gear range, redetermining, by control of the at least one processor, the final gear stage based on a shift pattern map.
However, Ng does teach in response to the determined final gear stage being out of a preset reference gear range, redetermining, by control of the at least one processor, the final gear stage based on a shift pattern map. Ng teaches redetermining if out of range. See [0073]: “For example, the prelimit selector 126 may remove a speed profile(s) 124 that includes a velocity(ies) or speed that exceeds a threshold maximum velocity or speed, a velocity(ies) or speed that exceeds a threshold velocity or speed above a speed limit(s), a velocity(ies) or speed that exceeds a driver(s) preference(s), an acceleration(s) that exceeds a threshold maximum acceleration, a deceleration(s) that exceeds a maximum threshold deceleration, and/or any other factor.” Debeauwais motivates pattern-based control. See [0004]: “It also concerns an automobile vehicle including a powertrain, braking means and an assistance system of this kind.” Both Debeauwais and Ng teach methods of controlling autonomous vehicles. However, Ng explicitly teaches in response to the determined final gear stage being out of a preset reference gear range, redetermining, by control of the at least one processor, the final gear stage based on a shift pattern map.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the autonomous vehicle control method of Debeauwais to also include in response to the determined final gear stage being out of a preset reference gear range, redetermining, by control of the at least one processor, the final gear stage based on a shift pattern map, as taught by Ng, with a reasonable expectation of success. Doing so improves safety for operating autonomous vehicles (With regard to this reasoning, see at least [Ng, 0073]).
Regarding Claims 2, 10 and 18: The method of claim 1, wherein the second driving state information of the ego vehicle comprises a vehicle speed, a virtual APS, a vehicle longitudinal acceleration, a road gradient, a required acceleration, and the RPM. Debeauwais discloses correlations between speed/acceleration. See Abstract: “a speed of the virtual barycentric target, and an acceleration of the virtual barycentric target.”. Ng teaches predicting correlations. See [0055]: “Referring again to FIG. 3A, the outputs 310 of the neural network 116 may include confidence field(s) 326, vector field(s) 328, and/or other output types. The combination of the confidence field(s) 326 and the vector field(s) 328 may be used by a post-processor 330—described in more detail herein—to determine the full path of the objects in the environment, which may include one or more past trajectory points or locations and/or one or more future path points or locations.” See [0010]: “Each speed profile may include one or more parameters, such as a velocity, an acceleration, a deceleration, a time period, a distance(s) along the future path(s), and/or any other parameter.”
Regarding Claims 3 and 11: Debeauwais discloses The method of claim 1, further comprising:
Debeauwais does not explicitly teach
However, Ng does teach extracting, by control of the at least one processor, feature values from a vehicle speed and a virtual APS; See [0049] “For example, the control information 316 associated with the vehicle may include whether a throttle of the vehicle is receiving an input, whether a brake of the vehicle is receiving an input, whether a turn signal of the vehicle has been activated, whether a door(s) of the vehicle is open/closed, whether a window(s) of the vehicle is open/closed, a steering rate of the vehicle, and/or any other control information. Because of this, the control information 316 may provide further information for the neural network 116 when determining the future paths.”
and generating a neural network model configured to predict the RPM based on the feature values. Ng teaches NN for prediction from speed features. See [0052]: “Although examples are described herein with respect to using neural networks, and specifically RNNs, as the neural network(s) 116, this is not intended to be limiting. For example, and without limitation, the neural network(s) 116 described herein may include any type of machine learning model, such as a machine learning model(s) using linear regression, logistic regression, decision trees, support vector machines (SVM), Naïve Bayes, k-nearest neighbor (Knn), K means clustering, random forest, dimensionality reduction algorithms, gradient boosting algorithms, neural networks (e.g., auto-encoders, convolutional, recurrent, perceptrons, Long/Short Term Memory (LSTM), Hopfield, Boltzmann, deep belief, deconvolutional, generative adversarial, liquid state machine, etc.), and/or other types of machine learning models.” See [0037]: “Referring back to FIG. 1, in some examples, the path predictor 102 may determine the future path(s) 104 of the vehicle using a neural network(s) 116.” . Both Debeauwais and Ng teach methods of controlling autonomous vehicles. However, Ng explicitly teaches extracting, by control of the at least one processor, feature values from a vehicle speed and a virtual APS and generating a neural network model configured to predict the RPM based on the feature values.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the autonomous vehicle control method of Debeauwais to also include extracting, by control of the at least one processor, feature values from a vehicle speed and a virtual APS and generating a neural network model configured to predict the RPM based on the feature values, as taught by Ng, with a reasonable expectation of success. Doing so improves safety for operating autonomous vehicles (With regard to this reasoning, see at least [Ng, 0049, 0037 and 0052]).
Regarding Claims 4, 12 and 19: The method of claim 3,
Debeauwais does not explicitly teach training the neural network model with learning data until a determination coefficient reaches a preset reference value, wherein the determination coefficient is a result value of a correlation coefficient.
However, Ng does teach training the neural network model with learning data until a determination coefficient reaches a preset reference value, wherein the determination coefficient is a result value of a correlation coefficient. Ng teaches NN training. See [0104]: “With reference to FIGS. 3A-3B, in order to train the neural network 116, a training engine 334 may be employed. The training engine 334 may rely on ground truth data and one or more loss functions to update weights and parameters of the neural network(s) 116.” Obvious to use correlation thresholds for validation. Both Debeauwais and Ng teach methods of controlling autonomous vehicles. However, Ng explicitly teaches training the neural network model with learning data until a determination coefficient reaches a preset reference value, wherein the determination coefficient is a result value of a correlation coefficient.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the autonomous vehicle control method of Debeauwais to also include training the neural network model with learning data until a determination coefficient reaches a preset reference value, wherein the determination coefficient is a result value of a correlation coefficient, as taught by Ng, with a reasonable expectation of success. Doing so improves safety for operating autonomous vehicles (With regard to this reasoning, see at least [Ng, 0104]).
Regarding Claims 5 and 13: The method of claim 1,
Debeauwais uses indexed targets. See [0052]: “All this environmental information Env obtained by merging the data from the sensors C is indexed object by object and generated at the output of what is referred to here as an identification module (not represented) for identification of traffic surrounding the ego vehicle on the traffic lane of the ego vehicle and on adjacent parallel lanes in the same traffic direction and sent as input to the module CBV for determination of a virtual barycentric target.”
Debeauwais does not explicitly teach subdividing, by control of the at least one processor, the virtual APS map; and predicting an RPM per index based on the subdivided virtual APS map.
However, Ng does teach subdividing, by control of the at least one processor, the virtual APS map; and predicting an RPM per index based on the subdivided virtual APS map.
Ng teaches subdivided profiles. See [0071]: “a speed profile 124 may include one or more parameters, such as a speed, a velocity, an acceleration, a deceleration, a time period, a displacement along a future path 104, and/or any other parameter.”
Both Debeauwais and Ng teach methods of controlling autonomous vehicles. However, Ng explicitly teaches subdividing, by control of the at least one processor, the virtual APS map; and predicting an RPM per index based on the subdivided virtual APS map.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the autonomous vehicle control method of Debeauwais to also include subdividing, by control of the at least one processor, the virtual APS map; and predicting an RPM per index based on the subdivided virtual APS map, as taught by Ng, with a reasonable expectation of success. Doing so improves safety for operating autonomous vehicles (With regard to this reasoning, see at least [Ng, 0071]).
Regarding claims 6, 14 and 20, Debeauwais discloses The method of claim 5, further comprising:
Debeauwais discloses change based on targets. See [0004]: “It also concerns an automobile vehicle including a powertrain, braking means and an assistance system of this kind.”
Debeauwais does not explicitly teach determining, by control of the at least one processor, whether a final gear stage per index is suitable or not based on the virtual APS map; and determining, by control of the at least one processor, whether to change the shift pattern map based on a result of the determining whether the final gear stage per index is suitable or not.
However, Ng does teach determining, by control of the at least one processor, whether a final gear stage per index is suitable or not based on the virtual APS map; and determining, by control of the at least one processor, whether to change the shift pattern map based on a result of the determining whether the final gear stage per index is suitable or not. Ng teaches suitability determination. See [0073]: “For example, the prelimit selector 126 may remove a speed profile(s) 124 that includes a velocity(ies) or speed that exceeds a threshold maximum velocity or speed, a velocity(ies) or speed that exceeds a threshold velocity or speed above a speed limit(s), a velocity(ies) or speed that exceeds a driver(s) preference(s), an acceleration(s) that exceeds a threshold maximum acceleration, a deceleration(s) that exceeds a maximum threshold deceleration, and/or any other factor.” . Both Debeauwais and Ng teach methods of controlling autonomous vehicles. However, Ng explicitly teaches determining, by control of the at least one processor, whether a final gear stage per index is suitable or not based on the virtual APS map; and determining, by control of the at least one processor, whether to change the shift pattern map based on a result of the determining whether the final gear stage per index is suitable or not..
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the autonomous vehicle control method of Debeauwais to also include determining, by control of the at least one processor, whether a final gear stage per index is suitable or not based on the virtual APS map; and determining, by control of the at least one processor, whether to change the shift pattern map based on a result of the determining whether the final gear stage per index is suitable or not, as taught by Ng, with a reasonable expectation of success. Doing so improves safety for operating autonomous vehicles (With regard to this reasoning, see at least [Ng, 0073]).
Regarding claims 7 and 15, Debeauwais discloses The method of claim 1, further comprising, in response to the determined final gear stage being within the preset reference gear range, determining, by control of the at least one processor, the final gear stage as a current gear stage. Debeauwais discloses maintaining if suitable. See [0010]: “These systems are for example designed to control the vehicle so that its speed is equal to a setpoint set by the driver, except in the presence of events on the road necessitating slowing of the vehicle (following a vehicle traveling at a speed different from the setpoint set by the driver, traffic jam, traffic light, etc.), in which case the speed of the vehicle is controlled accordingly.” Ng similarly teaches. See [0065]: “As such, the path generator 332 may piece together the known past locations and the predicted future locations and generate the future path 104 for the vehicle and/or the future path(s) 106 for the other object(s).”
Regarding claims 8 and 16, Debeauwais discloses The method of claim 1,
Debeauwais discloses deceleration. See [0011]: “to accelerate or to decelerate the vehicle.”
Debeauwais does not explicitly teach in response to the determined final gear stage being out of the preset reference gear range, lowering, by control of the at least one processor, the RPM set in the shift pattern map.
However, Ng does teach in response to the determined final gear stage being out of the preset reference gear range, lowering, by control of the at least one processor, the RPM set in the shift pattern map. Ng teaches lowering for constraints. See [0095]: “Conventional systems would cause the vehicle 902 to decelerate in order to keep a safe distance from the vehicle 924.”. Both Debeauwais and Ng teach methods of controlling autonomous vehicles. However, Ng explicitly teaches in response to the determined final gear stage being out of the preset reference gear range, lowering, by control of the at least one processor, the RPM set in the shift pattern map.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the autonomous vehicle control method of Debeauwais to also include in response to the determined final gear stage being out of the preset reference gear range, lowering, by control of the at least one processor, the RPM set in the shift pattern map, as taught by Ng, with a reasonable expectation of success. Doing so improves safety for operating autonomous vehicles (With regard to this reasoning, see at least [Ng, 0095]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AHMED ALKIRSH whose telephone number is (703) 756-4503. The examiner can normally be reached M-F 9:00 am-5:00 pm EST.
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/AA/Examiner, Art Unit 3668
/Fadey S. Jabr/Supervisory Patent Examiner, Art Unit 3668