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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claims are either directed to a system or a method, which is one of the statutory categories of invention. (Step 1: YES)
The examiner has identified system Claim 13 as the claim that represents the claimed invention for analysis and is similar to Claim 1 and Claim 20. Claim 13 recites the limitations of (whenever present additional elements are emphasized in bold and are considered to be parsed from the remaining abstract idea):
“A method for virtual test driving using a hierarchical reinforcement learning approach, the method comprising: receiving training data associated with characteristics regarding a race; generating one or more goals according to the training data; determining one or more functions of a vehicle to achieve the one or more goals based on control algorithms; training a driving model based on the training data, the one or more goals and the one or more functions; performing one or more simulations of a set of goals and a set of functions derived from the driving model based on first race characteristics; and generating results comprising an optimal vehicle setup, optimal goals and optimal functions according to the one or more simulations.”
Which is a process that, under its broadest which is a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) as a Mental process (concept performed in the human mind) and Certain Methods of Organizing Human Activity (fundamental economic practice – managing personal behavior or relationships or interactions between people.) training a classification model by using a transformer model (see, e.g., ¶56).
If a claim limitation, under its broadest reasonable interpretation (BRI), covers performance of the limitation as a certain method of a fundamental economic practice, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas.
Similarly if a claim limitation under its BRI, covers performance of the limitation in the human mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. (Claims can recite a mental process even if they are claimed as being performed on a computer Gottschalk v. Benson, 409 U.S. 63; "Courts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind." Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015).)
Accordingly, the claim recites an abstract idea. (Step 2A-Prong 1: YES. The claims are abstract)
This judicial exception is not integrated into a practical application. Limitations that are not indicative of integration into a practical application include: (1) Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05.f), (2) Adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05.g), (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05.h).
Claim 13 does not utilize any generic computer components in its current form, however in order to advance compact prosecution, will be assumed to also apply the same circuitry as applied in Claim 1 (Applicant should fix in the next action). Each step should positively recited to include “the one or more processors” performing each step.
Claim 1 includes one or more processors; and memory coupled to the one or more processors to store instructions.
Claim 20 includes a processor an non-transitory machine-readable medium.
The computer hardware is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts to no more than mere instructions to implement an abstract idea by adding the words “apply it” (or an equivalent) with the judicial exception. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Therefore claim 13 is directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are 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 because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using computer hardware amounts to no more than mere instructions to implement an abstract idea by adding the words “apply it” (or an equivalent) with the judicial exception. Mere instructions to implement an abstract idea on or with the use of generic computer components, cannot provide an inventive concept - rendering the claim patent ineligible. Thus claim 13 is not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
The dependent claims further define the abstract idea that is present in their respective independent claims and hence are abstract for at least the reasons presented above. The dependent claims do not include any additional elements, which are all generic computer components that further implement the abstract idea) that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination:
The dependent claims recite further steps that can be performed in the human mind.
Therefore, the dependent claims are directed to an abstract idea. Thus, the aforementioned claims are not patent-eligible.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Palanisamy et al. US 20200033868 (“Palanisamy”) in view of Lockel et al. “An Adaptive Human Driver Model for Realistic Race Car Simulations” (“Lockel,” cited by applicant”)
Re 1: Palanisamy teaches: A computing system for virtual test driving (¶¶6-19) using a hierarchical reinforcement learning approach (¶106) comprising
one or more processors 44 (Fig. 3, 5; ¶6-19, 27, 52); and
memory 46 coupled to the one or more processors to store instructions (Fig. 3; ¶52, which when executed by the one or more processors, cause the one or more processors to perform operations, the operations comprising:
receiving training data (abstract; claim 20: teaches collecting/pooling “driving experiences” into experience memory and sampling them for learning);
generating one or more goals according to the training data (¶90-97: discusses a long-term objective that is derived in conjunction with RL and its data);
determining one or more functions of a vehicle to achieve the one or more goals based on control algorithms (Fig. 10; ¶96-101: teaches an RL agent/policy mapping state/observations to actions);
training a driving model based on the training data, the one or more goals and the one or more functions (Fig. 10; ¶96-101: teaches learner modules training/updating policies using pooled experiences);
performing one or more simulations of a set of goals and a set of functions (¶91-92: teaching the agent-environment loop for RL);
and generating results comprising optimal goals and optimal functions according to the one or more simulations (Fig. 10; ¶91-92: teaches selecting/storing an optimal policy).
Palanisamy does not explicitly teach: associated with characteristics regarding a race; derived from the driving model based on first race characteristics; an optimal vehicle setup.
Lockel teaches (pp. 1-5) associated with characteristics regarding a race (note: teaches RL for racing); derived from the driving model based on first race characteristics (teaches race-driving simulations an explicitly addresses adaptations to “modifications to the vehicle setup”); an optimal vehicle setup (repeated simulations provides data on adjustments given RL algorithm).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to combine Palanisamy with Lockel's teachings in order to optimize race-driving control policies under race characteristics, by applying a computerized system to the specific problem of race card driving, thereby bringing a structured HRL set up to race car driving neural network analysis.
Palanisamy in view of Lockel discloses:
Re 2: wherein the training data comprises vehicle setup data, driver data and environment data (Lockel’s combination discloses a vehicle setup data along with driver and environment).
Re 3: wherein the vehicle is a race car (Lockel’s combination is explicitly for a race car).
Re 4: wherein the vehicle setup data comprises a mechanical and electrical setting of the vehicle (Lockel’s vehicle setup data includes all aspects of vehicle).
Re 5: wherein the driver data comprises a driver performance characteristic (Palanisamy teaching “collective driving experiences”).
Re 6: wherein the environment data comprises a racetrack layout, conditions of a racetrack and weather setting (Lockel teaches racetrack conditions and layout).
Re 7: wherein the one or more goals comprises a selection of an apex point, a braking point and an acceleration point (Lockel disclosing all of these).
Re 8: wherein the one or more functions comprises an action to be performed with the vehicle while the vehicle is in motion (Palanisamy clearly teaching modifying the vehicle while in motion).
Re 9: wherein the first race characteristics comprise at least one of a first vehicle setup, a first driver data or a first environment data (Lockel teaches all these parameters).
Re 10: wherein the operations further comprise displaying the results on a Graphical User Interface (GUI) (both teaching visual displays).
Re 11: wherein the operations further comprise: performing a second simulation of the results; and displaying the second simulation of the results on a GUI (Lockel teaches iterative simulations).
Re 12: wherein the operations further comprise: generating an adaptive controller that adapts to changes in characteristics of the race; generating second results comprising a second optimal vehicle setup, second optimal goals and second optimal functions according to the driving model and second race characteristics; and displaying the second results on a GUI (Fig. 10; ¶¶91-103).
Re 13: Palanisamy teaches A method for virtual test driving using a hierarchical reinforcement learning approach (¶106), the method comprising: receiving training data associated with characteristics (abstract; claim 20); generating one or more goals according to the training data (¶90-97: discusses a long-term objective that is derived in conjunction with RL and its data); determining one or more functions of a vehicle to achieve the one or more goals based on control algorithms (Fig. 10; ¶96-101: teaches an RL agent/policy mapping state/observations to actions); training a driving model based on the training data, the one or more goals and the one or more functions (Fig. 10; ¶96-101: teaches learner modules training/updating policies using pooled experiences); performing one or more simulations of a set of goals and a set of functions derived from the driving model (¶91-92: teaching the agent-environment loop for RL); and generating results comprising optimal goals and optimal functions according to the one or more simulations (Fig. 10; ¶91-92: teaches selecting/storing an optimal policy).
Palanisamy does not explicitly teach regarding a race; based on first race characteristics; an optimal vehicle setup.
Lockel teaches (pp. 1-5) regarding a race; based on first race characteristics; an optimal vehicle setup.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to combine Palanisamy with Lockel's teachings in order to optimize race-driving control policies under race characteristics, by applying a computerized system to the specific problem of race card driving, thereby bringing a structured HRL set up to race car driving neural network analysis.
Palanisamy in view of Lockel discloses:
Re 14: wherein the training data comprises vehicle setup data, driver data and environment data (Lockel’s combination discloses a vehicle setup data along with driver and environment).
Re 15: wherein the vehicle setup data comprises a mechanical and electrical setting of the vehicle (Lockel’s vehicle setup data includes all aspects of vehicle).
Re 16: wherein the driver data comprises a driver performance characteristic (Palanisamy teaching “collective driving experiences”).
Re 17: wherein the environment data comprises a racetrack layout, conditions of a racetrack and weather setting (Lockel teaches racetrack conditions and layout).
Re 18: wherein the one or more functions comprises an action to be performed with the vehicle while the vehicle is in motion (Palanisamy clearly teaching modifying the vehicle while in motion).
Re 19: further comprising: displaying the results on a Graphical User Interface (GUI); performing a second simulation of the results; and displaying the second simulation of the results on the GUI.
Re 20: Palanisamy teaches a non-transitory machine-readable medium 46 having instructions stored therein, which when executed by a processor 44 (Fig. 3, 5; ¶6-19, 27, 52), cause the processor to perform operations, the operations comprising: receiving training data; generating one or more goals according to the training data; determining one or more functions of a vehicle to achieve the one or more goals based on control algorithms; training a driving model based on the training data, the one or more goals and the one or more functions; performing one or more simulations of a set of goals and a set of functions; and generating results comprising optimal goals and optimal functions according to the one or more simulations.
Palanisamy does not explicitly teach, while Lockel teaches (pp. 1-5): associated with characteristics regarding a race; based on first race characteristics; generating results comprising an optimal vehicle setup.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to combine Palanisamy with Lockel's teachings in order to optimize race-driving control policies under race characteristics, by applying a computerized system to the specific problem of race card driving, thereby bringing a structured HRL set up to race car driving neural network analysis.
Conclusion
Relevant prior art considered:
US 20240086586 teaching a computer-implemented method for simulating vehicle data and improving driving scenario detection is provided. The method includes retrieving, from vehicle sensors, key parameters from real data of validation scenarios to generate corresponding scenario configurations and descriptions, transferring target scenario descriptions and validation scenario descriptions to target scenario scripts and validation scenario scripts, respectively, to create first raw simulation data pertaining to target scenario descriptions and second raw simulation data pertaining to validation scenario descriptions, training, by an adjuster network, a deep neural network model to minimize differences between the first raw simulation data and the second raw simulation data, refining the first and second raw simulation data of rare driving scenarios to generate rare driving scenario training data, and outputting the rare driving scenario training data to a display screen of a computing device to enable a user to train a scenario detector for an autonomic driving assistant system.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GERALD J SUFLETA II whose telephone number is (571)272-4279. The examiner can normally be reached M-F 9AM-6PM EDT/EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ABDULMAJEED AZIZ can be reached at (571) 270-5046. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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GERALD J. SUFLETA II
Primary Examiner
Art Unit 2875
/GERALD J SUFLETA II/Primary Examiner, Art Unit 2875