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 .
Election/Restrictions
Applicant’s election without traverse of claims 9-16 in the reply filed on 8/5/2024 is acknowledged.
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 9-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis-Step 1
Claim 9 is directed to a process Therefore, claims 9 is within at least one of the four statutory categories.
101 Analysis-Step2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, 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 claim 9 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites:
9. A cognition method using a complex network-based cognition system of an autonomous vehicle, characterized by comprising the following steps:
step 1): extracting a longitudinal driving characteristic parameter, a lateral driving characteristic parameter, and a mode shift characteristic parameter, constructing a driving style characteristic matrix C1, generating a random forest classifier Rf, inputting the driving style characteristic matrix C into the random forest classifier Rf, outputting a driving style category Karive through the random forest classifier Rf, and recognizing a driving style as an aggressive category, a peaceful category, or a conservative category;
step 2): constructing a time-varying complex dynamical network G as a complex environment model, to describe overall correlation characteristics of a complex environment; further establishing a node kinetic equation in the complex environment model; then combining a dynamical equation vector F(X) of all the nodes in the time- varying complex dynamical network G, a coupling matrix P(t) of the nodes in the time- varying complex dynamical network G, and a node inline vector H(X), to establish a node system kinetic equation of the time-varying complex dynamical network G to describe dynamic characteristics of the complex environment;
step 3): constructing four parameters of the nodes in the complex environment model: measure gj, degree k , node weight se, and importance I(i), and performing a differentiated analysis on the nodes by using a normal distribution graph, to implement a differentiated cognition of the nodes;
step 4): hierarchizing the nodes in the complex environment model by using an agglomerative algorithm, to implement a hierarchal, stepped cognition of the complex environment of the autonomous vehicle; and
step 5): measuring a disorder degree of the complex environment model by using system entropy and an entropy change according to a basic idea of an entropy theory, and describing an overall risk and a changing trend, to implement a global common state cognition.
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, “constructing, hierarchizing, measuring…” 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 in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, 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”):
9. A cognition method using a complex network-based cognition system of an autonomous vehicle, characterized by comprising the following steps:
step 1): extracting a longitudinal driving characteristic parameter, a lateral driving characteristic parameter, and a mode shift characteristic parameter, constructing a driving style characteristic matrix C1, generating a random forest classifier Rf, inputting the driving style characteristic matrix C into the random forest classifier Rf, outputting a driving style category Karive through the random forest classifier Rf, and recognizing a driving style as an aggressive category, a peaceful category, or a conservative category;
step 2): constructing a time-varying complex dynamical network G as a complex environment model, to describe overall correlation characteristics of a complex environment; further establishing a node kinetic equation in the complex environment model; then combining a dynamical equation vector F(X) of all the nodes in the time- varying complex dynamical network G, a coupling matrix P(t) of the nodes in the time- varying complex dynamical network G, and a node inline vector H(X), to establish a node system kinetic equation of the time-varying complex dynamical network G to describe dynamic characteristics of the complex environment;
step 3): constructing four parameters of the nodes in the complex environment model: measure gj, degree k , node weight se, and importance I(i), and performing a differentiated analysis on the nodes by using a normal distribution graph, to implement a differentiated cognition of the nodes;
step 4): hierarchizing the nodes in the complex environment model by using an agglomerative algorithm, to implement a hierarchal, stepped cognition of the complex environment of the autonomous vehicle; and
step 5): measuring a disorder degree of the complex environment model by using system entropy and an entropy change according to a basic idea of an entropy theory, and describing an overall risk and a changing trend, to implement a global common state cognition.
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 “extracting…,” the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer (vehicle controller) to perform the process. In particular, the extracting steps are recited at a high level of generality (i.e. as a general means of gathering vehicle data for use in the measuring step), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The control system is recited at a high level of generality and merely automates the evaluating step.
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 2019 PEG, 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 using a vehicle controller to perform the constructing and measuring… amounts to nothing more than applying the exception using a generic computer component. Generally applying an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitations of “extracting…,” the examiner submits that these limitations are insignificant extra-solution activities.
Further, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The additional limitations of “extracting longitudinal driving characteristic parameter…” are well-understood, routine, and conventional activities. Hence, the claim is not patent eligible.
Dependent claims 10-16 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 10-16 are not patent eligible under the same rationale as provided for in the rejection of claim 1.
Therefore, claims 9-16 are ineligible under 35 USC §101.
Allowable Subject Matter
Claims 9-16 would be allowable if rewritten or amended to overcome the rejections under 35 U.S.C. 101 set forth in this Office action.
The following is a statement of reasons for the indication of allowable subject matter:
The prior art does not disclose step 1): extracting a longitudinal driving characteristic parameter, a lateral driving characteristic parameter, and a mode shift characteristic parameter, constructing a driving style characteristic matrix C1, generating a random forest classifier Rf, inputting the driving style characteristic matrix C into the random forest classifier Rf, outputting a driving style category Karive through the random forest classifier Rf, and recognizing a driving style as an aggressive category, a peaceful category, or a conservative category;
step 2): constructing a time-varying complex dynamical network G as a complex environment model, to describe overall correlation characteristics of a complex environment; further establishing a node kinetic equation in the complex environment model; then combining a dynamical equation vector F(X) of all the nodes in the time- varying complex dynamical network G, a coupling matrix P(t) of the nodes in the time- varying complex dynamical network G, and a node inline vector H(X), to establish a node system kinetic equation of the time-varying complex dynamical network G to describe dynamic characteristics of the complex environment;
step 3): constructing four parameters of the nodes in the complex environment model: measure gj, degree k , node weight se, and importance I(i), and performing a differentiated analysis on the nodes by using a normal distribution graph, to implement a differentiated cognition of the nodes;
step 4): hierarchizing the nodes in the complex environment model by using an agglomerative algorithm, to implement a hierarchal, stepped cognition of the complex environment of the autonomous vehicle; and
step 5): measuring a disorder degree of the complex environment model by using system entropy and an entropy change according to a basic idea of an entropy theory, and describing an overall risk and a changing trend, to implement a global common state cognition.
The closest prior art on record, Soliman US 2019/0004526 discloses a efficiency autonomous driving strategy which accounts for the propulsion system efficiency and energy consumption during vehicle motion planning function required for autonomous driving. The system calculates the required energy and total efficiency for various possible vehicle path/motion plans being considered by the autonomous driving controller.
The closes prior art on record, Huang US 2010/0023223 discloses an adaptive vehicle control system that adapts to one or both of driving environment and the driver's driving characteristics.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IMRAN K MUSTAFA whose telephone number is (571)270-1471. The examiner can normally be reached Mon-Fri 9-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, James J Lee can be reached at 571-270-5965. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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IMRAN K. MUSTAFA
Primary Examiner
Art Unit 3668
/IMRAN K MUSTAFA/Primary Examiner, Art Unit 3668
4/3/2026