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 .
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 1-7 are rejected under 35 U.S.C. 101
because the claimed invention is directed to an abstract idea without significantly
more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be
determined whether the claim is directed to one of the four statutory categories of
invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the
claim does fall within one of the statutory categories, the second step in the analysis is
to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A
analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined
whether or not the claims recite a judicial exception (e.g., mathematical concepts,
mental processes, certain methods of organizing human activity). If it is determined in
Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the
second prong (Step 2A, Prong 2), where it is determined whether or not the claims
integrate the judicial exception into a practical application. If it is determined at step 2A,
Prong 2 that the claims do not integrate the judicial exception into a practical
application, the analysis proceeds to determining whether the claim is a patent-eligible
application of the exception (Step 2B). If an abstract idea is present in the claim, any
element or combination of elements in the claim must be sufficient to ensure that the
claim integrates the judicial exception into a practical application, or else amounts to
significantly more than the abstract idea itself. Applicant is advised to consult the 2019
PEG for more details of the analysis.
Step 1
According to the first part of the analysis, in the instant case, claims 1-7 are directed to a closed-loop online self-learning architecture. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter). Step 2A,
Step 2A, Prong 1
Following the determination of whether or not the claims fall within one of the four
categories (Step 1), it must be determined if the claims recite a judicial exception (e.g.
mathematical concepts, mental processes, certain methods of organizing human
activity) (Step 2A, Prong 1). In this case, the claims are determined to recite a judicial
exception as explained below.
Regarding Claim 1 the claim recites
five data closed-loop links, wherein the five data closed-loop links include an Over-the-Air Technology (OTA) closed loop, an online learning closed loop, an algorithm evolution closed loop, a self-adversarial improvement closed loop, and a cloud coevolution closed loop, wherein according to current characteristics of a self-evolution process of an algorithm, the five data closed-loop links are subjected to overall management through an upper logical switching layer, finally achieving closed-loop evolution of an automatic driving algorithm.
The claims recite a mental process. As set forth in MPEP 2106.04(a)(2)(III)(C), “Claims can recite a mental process even if they are claimed as being performed on a computer”. These are recited at a high level such that they could be performed mentally, and they are also disclosed as a human user performing these functions, simply using a computer as a tool-see spec, page 2-8, Fig. 1. Thus, the claim recites abstract ideas.
Step 2A, Prong 2
Following the determination that the claims recite a judicial exception, it must be
determined if the claims recite additional elements that integrate the exception into a
practical application of the exception (Step 2A, Prong 2). In this case, after considering
all claim elements individually and as an ordered combination, it is determined that the
claims do not include additional elements that integrate the exception into a practical
application of the exception as explained below.
In Prong Two, a claim is evaluated as a whole to determine whether the recited judicial exception is integrated into a practical application of that exception. A claim is not “directed to” a judicial exception, and thus is patent eligible, if the claim as a whole integrates the recited judicial exception into a practical application of that exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or 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 judicial exception. MPEP 2106.04(d). The claims recite an abstract idea and further the claims as a whole does not integrate the recited judicial exception into a practical application of the exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or 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 judicial exception. MPEP 2106.04(d).
Regarding Claim 1 the claim recites
This limitation is understood to be generic computer equipment and mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.0S(f))
Step 2B
Based on the determination in Step 2A of the analysis that the claims are
directed to a judicial exception, it must be determined if the claims contain any element
or combination of elements sufficient to ensure that the claim amounts to significantly
more than the judicial exception (Step 2B). In this case, after considering all claim
elements individually and as an ordered combination, it is determined that the claims do
not include additional elements that are sufficient to amount to significantly more than
the judicial exception for the same reasons given above in the Step 2A, Prong 2
analysis. Furthermore, each additional element identified above as being insignificant
extra-solution activity is also well-known, routine, conventional as described below.
Claim 1: The claims do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than generic computing components and field of use/technological environment which do not amount to significantly more than the abstract idea. The underlying concept merely receives information, analyzes it, and store the results of the analysis – this concept is not meaningfully different than concepts found by the courts to be abstract (see Electric Power Group, collecting information, analyzing it, and displaying certain results of the collection and analysis; see Cybersource, obtaining and comparing intangible data; see Digitech, organizing information through mathematical correlations; see Grams, diagnosing an abnormal condition by performing clinical tests and thinking about the results; see Cyberfone, using categories to organize store and transmit information; see Smartgene, comparing new and stored information and using rules to identify options). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as a combination do not amount to significantly more than the abstract idea. For example, claim 1 recites the additional elements of five data closed-loop links, wherein the five data closed-loop links include an Over-the-Air Technology (OTA) closed loop, an online learning closed loop, an algorithm evolution closed loop, a self-adversarial improvement closed loop, and a cloud coevolution closed loop, wherein according to current characteristics of a self-evolution process of an algorithm, the five data closed-loop links are subjected to overall management through an upper logical switching layer, finally achieving closed-loop evolution of an automatic driving algorithm. These elements are recited at a high level of generality and are well-understood, routine, and conventional activities in the computer art. Generic computers performing generic computer functions, without an inventive concept, do not amount to significantly more than the abstract idea. Looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims do not amount to significantly more than the abstract idea itself.
Step 2A/2B Prong 2 Dependent Claims
Regarding to claim 2
Claim 2 merely recite other additional elements that define OTA closed loop which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 3
Claim 3 merely recite other additional elements that define online learning closed loop which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 4
Claim 4 merely recite other additional elements that define algorithm evolution closed loop which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 5-6
Claim 5-6 merely recite other additional elements that define self-adversarial improvement closed loop which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 7
Claim 7 merely recite other additional elements that define cloud coevolution closed loop which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-7 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Urtasun et al. (Urtasun)US 2025/0148725
In regard to claim 1, Urtasun disclose A closed-loop online self-learning architecture applied to an autonomous vehicle, (Fig. 1, [0018]-[0026] autonomous system that learns how to interact in the real world for self-driving vehicles with closed-loop simulator) comprising five data closed-loop links, wherein the five data closed-loop links include an Over-the-Air Technology (OTA) closed loop, an online learning closed loop, an algorithm evolution closed loop, a self-adversarial improvement closed loop, and a cloud coevolution closed loop, ([0018]-[0039] [0055]-[0065] [0073]-[0080] including sensor simulation models with simulated env. state, actor models with what if scenarios, asset models, etc. with closed loop evaluator for closed-loop simulation with closed-loop evaluations to improve autonomy, adversarial mode to modifies the actors to perform adversarial and closed loop training mode to optimize automatically driving performance with scalable improvement. Note: please use functional language to describe the invention, non-functional language does not have much patent weight, call to discuss if necessary) wherein according to current characteristics of a self-evolution process of an algorithm, the five data closed-loop links are subjected to overall management through an upper logical switching layer, finally achieving closed-loop evolution of an automatic driving algorithm. ([0018]-[0039][0045] [0050]-[0070] [0073]-[0080] the virtual driver and actor models update their behavior at each time step based on the current driving scene based on a procedurally generated algorithm, the modes/models/phases can be selected by the simulator which include controller to configure various configurations to achieve the scalable improvement for the automatic driving AI algorithm.)
In regard to claim 2, Urtasun disclose The closed-loop online self-learning architecture applied to an autonomous vehicle according to claim 1,
Urtasun disclose wherein the OTA closed loop specifically involves: a vehicle side of the autonomous vehicle transmitting a large amount of data collected by a sensor to a cloud side; an algorithm engineer extracting and organizing the large amount of data collected for model training and test evaluation; and after achieving phased improvement of the algorithm through the acquired data, a technician performing a version update and deploying a new model. ([0020]-[0026][0030]-[0045] [0053]-[0081][0090]-[0091] sensor models transmit data gathered the sensors from the real world, such as vehicle related data to a data repository, the scenario spec. may be specified by the user and data can be obtained and organized, the simulator can operate in multiple phases as selected to satisfies the requirements through the data gathered, the parameters of the virtual drive are updated to improve performance to become a smarter driver, the autonomous system may be deployed based on the requirement of success criterion is satisfied by the user with the software release under test)
In regard to claim 3, Urtasun disclose The closed-loop online self-learning architecture applied to an autonomous vehicle according to claim 1,
Urtasun disclose wherein the online learning closed loop involves: using sequential incoming data for learning and updates at each step during practical applications of the algorithm; the online learning closed loop specifically comprises two parts which are model training and test evaluation, ([0020]-[0029] [0031]-[0045] train and test a virtual driver of an autonomous system to create evaluation variations to provide insight to the virtual driver’s performance based on each time steps of data and update at each step during the simulation) wherein a quantitative evaluation result of self-evolution capability, namely algorithm performance, is obtained through the test evaluation; ([0020]-[0029] [0031]-[0045][0057]-[0065] the model performance is evaluated with performance through test evaluation) when the algorithm performance has not improved to generalized learning convergence, the online learning closed loop switches to the algorithm evolution closed loop to achieve further evolution of the algorithm; ([0020]-[0029] [0031]-[0045][0057]-[0065] when the virtual driver not have performed optimally, the closed loop training is used to provide feedback to the virtual driver to learn and improve performance with various mode selector which can be selected) when the algorithm performance has improved to the generalized learning convergence, the online learning closed loop switches to the self-adversarial improvement closed loop. ([0020]-[0029] [0031]-[0045][0057]-[0065] the virtual driver’s performance is improved based on satisfied requirement of success criteria, the selector mode can by selected to perform adversarial mode in closed loop evaluation)
In regard to claim 4, Urtasun disclose The closed-loop online self-learning architecture applied to an autonomous vehicle according to claim 1,
Urtasun disclose wherein the algorithm evolution closed loop involves: achieving further evolution of the algorithm performance by adjusting hyperparameters of the learning algorithm and structural parameters of a neural network, and switching to the online learning closed loop of a next round. ([0020]-[0029] [0031]-[0045][0055]-[0065] train and test a virtual driver of an autonomous system to create evaluation variations to provide insight to the virtual driver’s performance with optimizable parameters using Bayesian optimization or gradient estimation methodologies and optimize analytical vehicle model parameters or learn parameters of a NN and changed to the different closed loop learning of next iteration with mode selector)
In regard to claim 5, Urtasun disclose The closed-loop online self-learning architecture applied to an autonomous vehicle according to claim 1,
Urtasun disclose wherein the self-adversarial improvement closed loop involves: the autonomous vehicle operating in a real world and a virtual world simultaneously, jointly dealing with real and virtual traffic scenarios, which specifically comprises the following steps: ([0018] -[0029] [0031]-[0045][0055]-[0065] [0079]-[0083] autonomous driver operating in real world env. and virtual simulation of the real world scenarios (digital twins) concurrently and deal with real and virtual scenarios)
S1: determining, through a comprehensive evaluation of scenario task complexity and algorithm performance quantification, whether a current scenario exceeds an operational design domain of the automatic driving algorithm; ([0020]-[0029] [0031]-[0045][0055]-[0065] evaluate the variety of scenarios and patterns including edge cases if the model is satisfy the requirement of success performance criterion for a particular scenario based on the metric information related to a degree of breaking the rule with a score)
S2: performing parametric design on a scenario to obtain a parametric representation of scenario reconstruction; ([0020]-[0029] [0031]-[0045][0055]-[0068] the requirement of a particular scenario with scenario reconstruction with various optimizable parameters)
S3: generating an adversarial scenario on the basis of an enhanced learning method or an adversarial learning method, and injecting the adversarial scenario into a virtual scenario generation library; ([0020]-[0029] [0031]-[0045][0055]-[0068] generate an adversarial scenario on the basis of the adversarial mode, and modifying the actors to perform the adversarial scenario with the scenario specification stored in the data repository)
S4: combining the virtual scenario generation library, a typical standard data set, and real vehicle test data to form a data set library; ([0020]-[0029] [0031]-[0045] the scenario specification can by defined by a user and combine the various data into a data repository, such as, desired real world data log with simulated or training data, etc.)
and
S5: achieving an adversarial-enhanced data closed loop on the basis of the data set library by relying on a virtual and reality co-design. ([0020]-[0029] [0031]-[0045] the various data are generated by combining various data and scenario specification from real world and virtual world data based on the data repository)
In regard to claim 6, Urtasun disclose The closed-loop online self-learning architecture applied to an autonomous vehicle according to claim 5,
Urtasun disclose wherein the self-adversarial improvement closed loop closes data to a real vehicle operation level through an automatic scenario reconstruction technology and a data marking technology on the basis of characteristics of a real world and characteristics of virtual simulation; ([0020]-[0029] [0031]-[0045][0050]-[0067] closed loops closes data to a real world operation through scenario reconstruction with what-if scenarios did not occur in the real-world based on the propeties of the real world and simulation)
the real world comprises collecting perception data and improving the performance of a perception algorithm, and at the same time, supplementing and enriching the data set library by identifying and capturing an edge scenario; ([0019]-[0029] [0031]-[0045] [0050]-[0072] [0079]-[0082]collecting real work data and improving performance of the sensor model and combining various data and scenario data into data repository, such as by capturing rare edge cases)
the virtual simulation is used for generating the adversarial scenario, and achieving better and reasonable responses by training an automatic driving decision-making and planning algorithm in real time; ([0019]-[0029] [0031]-[0045] [0050]-[0072] [0079]-[0083] simulation generating adversarial scenario and improve reaction in real time by training the model)
in a framework of the self-adversarial improvement closed loop, an automatic driving system deals with more real-world scenarios by gradually and safely expanding the operational design domain thereof, and achieves real-time updates of virtual and real transparency until generation of virtual simulation scenarios is completely closed, thereby achieving the ultimate goal of safe automatic driving in the real world. ([0019]-[0029] [0031]-[0045] [0050]-[0072] [0079]-[0083] reconstruct digital twins of real work scenarios automatically and combine real world data and simulated data to create various scenarios and update the virtual scenarios until using all simulation scenarios to do closed-loop simulation to optimize the end objective of driving performance)
In regard to claim 7, Urtasun disclose The closed-loop online self-learning architecture applied to an autonomous vehicle according to claim 1,
Urtasun disclose wherein the cloud coevolution closed loop provides a multi-vehicle fast coevolution framework comprising a combined model training policy and a combined or local model update policy, thereby achieving efficient training resource sharing in the cloud coevolution. ([0019]-[0029] [0031]-[0045] [0089]-[0092] the autonomous system includes self-driving vehicles and includes assessing performance by applying rules with each rule associated with metric information relates to a degree of breaking the rule with a corresponding score and optimizable parameters and data can be used by multiple computers in a cloud system to achieve the training goal. note: thereby is intended use language which there is not much patent weight, please use functional language to describe the invention.)
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
U.S. Patent Documents PATENT DATE INVENTOR(S) TITLE
US 20190367025 A1 2019-12-05 Pathak et al.
ADAPTIVE LONGITUDINAL CONTROL USING REINFORCEMENT LEARNING
Pathak et al. disclose system for controlling a vehicle includes a first sensor for detecting at least one environment characteristic. The system also includes a driver characteristic input device configured to receive at least one driver characteristic corresponding to a driving style of a driver. The system also includes a controller that includes a reinforcement learning adaptive cruise control that is in communication with the first sensor and the driver characteristic input device, the reinforcement learning adaptive cruise control being configured to: determine a target behavior for the vehicle based on the at least one environment characteristic and the at least one driver characteristic; and selectively control the vehicle based on the target behavior… see abstract.
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XUYANG XIA
Primary Examiner
Art Unit 2143
/XUYANG XIA/Primary Examiner, Art Unit 2143
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