DETAILED ACTION
1. Claims 1-20 have been presented for examination.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
3. The information disclosure statement (IDS) submitted on 7/20/23 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the Examiner has considered the IDS as to the merits.
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.
4. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
i) In view of Step 1 of the analysis, claim(s) 1 is directed to a statutory category as a process, claim 9 is directed to a statutory category as a machine, and claim 15 is directed to an article of manufacture as a non-transitory machine-readable medium, which each represent a statutory category of invention. Therefore, claims 1-20 are directed to patent eligible categories of invention.
ii) In view of Step 2A, Prong One, claims 1, 9, and 15 recite the abstract idea of predicting driver trajectory which constitutes an abstract idea based on Mental Processes based on concepts performed in the human mind, or with the aid of pencil and paper as well as and alternatively as Mathematical Concepts including mathematical formulas or equations as well as calculations.
As per claim 1, and similarly recited in claims 9 and 15, the limitation of "determining a vehicle state for at least one location on the race track based on the received driver data;” would be analogous to a person evaluating a state of a vehicle based on given data and thus fall under Mental Processes. Thus, the claims recite the abstract idea of a mental process performed in the human mind, or with the aid of pencil and paper.
As per claim 1, and similarly recited in claims 9 and 15, the limitation of “determining expert driver data representing inputs of an expert driver at the determined vehicle state for the at least one location;” would be analogous to a person evaluating a state of a driver based on given data and thus fall under Mental Processes. Thus, the claims recite the abstract idea of a mental process performed in the human mind, or with the aid of pencil and paper.
As per claim 1, and similarly recited in claims 9 and 15, the limitation of “predicting how the expert driver would proceed from each current vehicle state based on the expert driver data;” would be analogous to a person predicting a state of a driver based on given data and thus fall under Mental Processes. Thus, the claims recite the abstract idea of a mental process performed in the human mind, or with the aid of pencil and paper. Alternatively, the limitation can be construed as Mathematical Concepts including mathematical formulas or equations as well as calculations.
As per claim 1, and similarly recited in claims 9 and 15, the limitation of “mapping the current vehicle state at the at least one location to a recommended trajectory from each current vehicle state based on predicting how the expert driver would proceed;” would be analogous to a person predicting a state of a vehicle based on given data and thus fall under Mental Processes. Thus, the claims recite the abstract idea of a mental process performed in the human mind, or with the aid of pencil and paper. Alternatively, the limitation can be construed as Mathematical Concepts including mathematical formulas or equations as well as calculations.
As to claims 9 and 15, other than reciting “a processor,” nothing in the claim element precludes the step from practically being performed in the mind.
Dependent claims 2-8, 10-14, and 16-20 further narrow the abstract ideas, identified in the independent claims.
iii) In view of Step 2A, Prong Two, the judicial exception is not integrated into a practical application. In Claims 9 and 15, the additional element of “a processor”, and the “non-transitory machine-readable medium”, in claim 15, merely uses a computer device as a tool to perform the abstract idea. (MPEP 2106.05(f)) The limitation in claim 1, and similarly recited in claims 9 and 15 of “receiving driver data representing driver inputs to a vehicle;” are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(f)) Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process) does not integrate a judicial exception into a practical application. (MPEP 2106.05(f)(2)) Additionally the limitation of “receiving driver data representing driver inputs to a vehicle;” in claims 1, 9, and 15, alternatively can be viewed as insignificant extra-solution activity, specifically pertaining to mere data gathering/output necessary to perform the abstract idea (MPEP 2106.05(g)) and is not sufficient to integrate the judicial exception into a practical application. This is akin to selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, which has been identified as extra solution activity. Therefore, the judicial exception is not integrated into a practical application.
Dependent claims 2-8, 10-14, and 16-20 further narrow the abstract ideas, identified in the independent claims and do not introduce further additional elements for consideration beyond those addressed above.
iv) In view of Step 2B, claims 1, 9 and 15 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claims 9 and 15, the additional element of “a processor”, and the “non-transitory machine readable medium”, in claim 15, merely uses a computer device as a tool to perform the abstract idea. (MPEP 2106.05(f)) The limitation in claim 1, and similarly recited in claims 9 and 15 of “receiving driver data representing driver inputs to a vehicle;” are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(f)) Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process) does not integrate a judicial exception into a practical application. (MPEP 2106.05(f)(2)) Additionally the limitation of “receiving driver data representing driver inputs to a vehicle;” in claims 1, 9, and 15, alternatively can be viewed as an insignificant extra-solution activity, specifically pertaining to mere data gathering/output necessary to perform the abstract idea (MPEP 2106.05(g)) and is not sufficient to integrate the judicial exception into a practical application. This is akin to selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, which has been identified as extra solution activity. Therefore, the claim as a whole does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, when considered alone or in combination, do not amount to significantly more than the judicial exception. As stated in Section I.B. of the December 16, 2014 101 Examination Guidelines, “[t]o be patent-eligible, a claim that is directed to a judicial exception must include additional features to ensure that the claim describes a process or product that applies the exception in a meaningful way, such that it is more than a drafting effort designed to monopolize the exception.”
The dependent claims include the same abstract ideas recited as recited in the independent claims, and merely incorporate additional details that narrow the abstract ideas and fail to add significantly more to the claims.
Dependent claims 2, 10, and 16 further recites the display of information which represents mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(f)) Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process) does not integrate a judicial exception into a practical application. (MPEP 2106.05(f)(2)) Alternatively the limitation can be viewed as an insignificant extra-solution activity, specifically pertaining to mere data gathering/output necessary to perform the abstract idea (MPEP 2106.05(g)) and is not sufficient to integrate the judicial exception into a practical application.
Dependent claims 3 and 4 further recites the display of information which represents mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(f)) Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process) does not integrate a judicial exception into a practical application. (MPEP 2106.05(f)(2)) Alternatively the limitation can be viewed as an insignificant extra-solution activity, specifically pertaining to mere data gathering/output necessary to perform the abstract idea (MPEP 2106.05(g)) and is not sufficient to integrate the judicial exception into a practical application.
Dependent claims 5, 11, and 18 further recites the evaluation and determination of the race track which merely narrows the abstract idea identified as a mental process and/or mathematical concepts including mathematical formulas or equations as well as calculations.
Dependent claims 6, 12, and 19 further recites the categorization of data of the race track which merely narrows the abstract idea identified as a mental process and/or mathematical concepts including mathematical formulas or equations as well as calculations.
Dependent claims 7, 13, and 20 further recites the calculation of minimized data which merely narrows the abstract idea identified as a mental process and/or mathematical concepts including mathematical formulas or equations as well as calculations.
Dependent claims 8 and 14 further recites the training of a machine learning model which represents mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(f)) Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process) does not integrate a judicial exception into a practical application. (MPEP 2106.05(f)(2))
Dependent claim 17 further recites the calculation of an updated trajectory which merely narrows the abstract idea identified as a mental process and/or mathematical concepts including mathematical formulas or equations as well as calculations.
v) Accordingly, claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without anything significantly more.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
5. Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
i) The term “expert” in claim 1 is a relative term which renders the claim indefinite. The term “expert” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. This rejection further applies to all instances of the term “expert” as recited in claim 4, 7, 9, 12, 13, 15, 17, and 20.
Appropriate correction is required.
All claims dependent upon a rejected base claim are rejected by virtue of their dependency.
Claim Rejections - 35 USC § 102
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(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.
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 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.
6. Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being clearly anticipated by Garlick, Sam, and Andrew Bradley. "Real-time optimal trajectory planning for autonomous vehicles and lap time simulation using machine learning." Vehicle System Dynamics 60.12 (2022): 4269-4289.
Regarding Claim 1: The reference discloses A method for optimizing a driver's trajectory around a race track, comprising:
receiving driver data representing driver inputs to a vehicle; (Page 4270, 2nd paragraph, “TheQSS approach typically requires pre-existing knowledge of a target path, which is usually recreated from logged data of the line taken by a professional racing driver [6] – however, ‘even with the aid of global positioning systems, the task is both expensive and rather diffi-cult’ [7]”)
determining a vehicle state for at least one location on the race track based on the received driver data; (Page 4270, 2nd paragraph, “The majority of LTS in use by race teams are based around Quasi-Steady-State (QSS)approaches [4] – where a vehicle model follows a target path (typically discretised into aseries of curves of varying radii) at the highest possible speed, obtained through iteration.This method can be further extended to incorporate transient vehicle behaviour [5].”)
determining expert driver data representing inputs of an expert driver at the determined vehicle state for the at least one location; (Page 4270, 2nd paragraph, “TheQSS approach typically requires pre-existing knowledge of a target path, which is usually recreated from logged data of the line taken by a professional racing driver [6] – however, ‘even with the aid of global positioning systems, the task is both expensive and rather diffi-cult’ [7]”)
predicting how the expert driver would proceed from each current vehicle state based on the expert driver data; and (Page 4271, 2nd paragraph, “This paper presents an ANN approach to predict the ideal racing line, aimed at reduc-ing the calculation time by several orders of magnitude. The network can be trained ondata from any existing method of optimal trajectory generation, thus facilitating predic-tions based upon highly complex models to be made within milliseconds.”)
mapping the current vehicle state at the at least one location to a recommended trajectory from each current vehicle state based on predicting how the expert driver would proceed. (Figures 6-9 showing the racing line of the vehicle as well as the lateral deviations from the track centerline)
Regarding Claim 2: The reference discloses The method of claim 1, further comprising displaying the recommended trajectory on a user interface as the driver simulates driving around the race track. (Figures 6-9 showing the racing line of the vehicle as well as the lateral deviations from the track centerline)
Regarding Claim 3: The reference discloses The method of claim 2, further comprising updating the displayed recommended trajectory when the driver changes vehicle state. (Figures 6-9 showing the racing line of the vehicle as well as the lateral deviations from the track centerline)
Regarding Claim 4: The reference discloses The method of claim 3, wherein updating the displayed recommended trajectory comprises determining how the expert driver would proceed from the changed vehicle state and mapping a new recommended trajectory. (Figures 6-9 showing the racing line of the vehicle as well as the lateral deviations from the track centerline. See also the predicted racing line vs optimal control method)
Regarding Claim 5: The reference discloses The method of claim 1, further comprising segmenting the race track and categorizing each segment of the race track. (Figure 1, with lines to divide the circuit)
Regarding Claim 6: The reference discloses The method of claim 5, further comprising categorizing the driver data based on a corresponding segment. (Figure 1, with data based on each segment)
Regarding Claim 7: The reference discloses The method of claim 1, wherein determining how the expert driver would proceed from each current vehicle state is based on minimizing a lap time around the race track. (Page 4284, 4th paragraph “The method presented in this paper could be coupled with a time-optimal simulator toprovide an exceptionally fast, accurate initial approximation for use as pre-calculation priorto fine-tuning with further (e.g. minimum-time) optimisations.” See also last paragraph on page 4281)
Regarding Claim 8: The reference discloses The method of claim 1, further comprising training a machine learning model with reinforced learning and regulating the machine learning model with additional simulation data to match human actions. (Page 4281, last paragraph, “The trajectories predicted by the ANN are qualitatively similar to those calculated using the OCP method, following near-identical paths at most apexes – where it has the most impact upon lap time [27] – owing to the relative consistency of the optimal mid-corner racing line in the training data. Maximum deviations between the ANN’s prediction and the OCP method tend to occur in areas with more complex and unusual features – e.g. the straight with a subtle bend prior to the upcoming corner at Nürburgring GP, and the complex combination of consecutive turns impacting upon each other (Figure 8). Larger sustained deviations tend to occur during straights – however, this behaviour is common to many methods of calculating the optimal line and is considered to be of lesser importance as a difference in vehicle position during a straight results in minimal impact upon lap time[18,49]”)
Regarding Claim 9: See rejection for claim 1.
Regarding Claim 10: The reference discloses The system of claim 9, wherein the instructions further cause the processor to display the one or more recommended vehicle settings on a user interface as the driver simulates driving around the race track. (See rejection for claim 2)
Regarding Claim 11: The reference discloses The system of claim 9, wherein the instructions further cause the processor to segment the race track and categorize each segment of the race track. (See rejection for claim 5)
Regarding Claim 12: The reference discloses The system of claim 11, wherein determining how the expert driver would proceed is based on a corresponding category of segment of the race track. (See rejection for claim 6)
Regarding Claim 13: The reference discloses The system of claim 9, wherein determining how the expert driver would proceed from each current vehicle state is based on minimizing a lap time around the race track. (See rejection for claim 7)
Regarding Claim 14: The reference discloses The system of claim 9, wherein the instructions further cause the processor to train a machine learning model with reinforced learning and regulate the machine learning model with additional simulation data to match human actions. (See rejection for claim 8)
Regarding Claim 15: See rejection for claim 1.
Regarding Claim 16: The reference discloses The non-transitory machine-readable medium of claim 15, wherein the instructions further cause the processor to update the displayed recommended trajectory when the driver changes vehicle state. (See rejection for claim 2)
Regarding Claim 17: The reference discloses The non-transitory machine-readable medium of claim 16, wherein the instructions further cause the processor to determine how the expert driver would proceed from the changed vehicle state and mapping a new recommended trajectory. (See rejection for claims 3 and 4)
Regarding Claim 18: The reference discloses The non-transitory machine-readable medium of claim 15, wherein the instructions further cause the processor to segment the race track and categorize each segment of the race track. (See rejection for claim 5)
Regarding Claim 19: The reference discloses The non-transitory machine-readable medium of claim 15, wherein the instructions further cause the processor to categorize the data based on a corresponding segment. (See rejection for claim 6)
Regarding Claim 20: The reference discloses The non-transitory machine-readable medium of claim 15, wherein determining how the expert driver would proceed from each current vehicle state is based on minimizing a lap time around the race track. (See rejection for claim 7)
Conclusion
7. All Claims are rejected.
8. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
i) N. R. Kapania and J. C. Gerdes, "Learning at the Racetrack: Data-Driven Methods to Improve Racing Performance Over Multiple Laps," in IEEE Transactions on Vehicular Technology, vol. 69, no. 8, pp. 8232-8242, Aug. 2020, doi: 10.1109/TVT.2020.2998065.
ii) Evans, Benjamin David, Herman Arnold Engelbrecht, and Hendrik Willem Jordaan. "High-speed autonomous racing using trajectory-aided deep reinforcement learning." IEEE Robotics and Automation Letters 8.9 (2023): 5353-5359.
iii) Capo, Emilio, and Daniele Loiacono. "Short-term trajectory planning in torcs using deep reinforcement learning." 2020 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2020.
9. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Saif A. Alhija whose telephone number is (571) 272-8635. The examiner can normally be reached on M-F, 10:00-6:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Renee Chavez, can be reached at (571) 270-1104. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300. Informal or draft communication, please label PROPOSED or DRAFT, can be additionally sent to the Examiners fax phone number, (571) 273-8635.
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SAA
/SAIF A ALHIJA/Primary Examiner, Art Unit 2186
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