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 .
Information Disclosure Statement
The references listed on the IDS filed 1/3/2025 and 3/4/2025 have been considered by the Examiner.
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.
Claim 10 is 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 10 is directed to a method of determining the determining the influence of a vehicle on the surrounding vehicles (i.e., a process). Therefore, claim 1 is within at least one of the four statutory categories.
101 Analysis – Step 2A, 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 1 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 10 recites:
A method comprising:
predicting an influence of one road user on at least one other road user by evaluating traffic scenarios using a trained artificial neural network,
wherein the artificial neural network is trained using recorded traffic scenarios, wherein the recorded traffic scenarios include several road users and the recorded traffic scenarios are labelled with score values representing an influence of one road user of the several road users by other road users of the several road users,
a respective score value for one road user of the several road users with respect to another road user of the several road users is calculated based on a determination of a deviation between two trajectories of the one road user of the several road users,
wherein one of the two trajectories is a detected real trajectory that the one road user of the several road users actually takes in a respective recorded traffic scenario, and
wherein a second of the two trajectories is a simulated trajectory determined in a simulation and representing a trajectory that the one road user would take in a same traffic scenario if the another road user of the several road users were not present.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers an activity of the human mind. For example, “predicting…” in the context of the claim comprises an analysis of data. “a respective score… is calculated…” in the context of the claim comprises again comprises mere data analysis.
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”):
A method comprising:
predicting an influence of one road user on at least one other road user by evaluating traffic scenarios using a trained artificial neural network,
wherein the artificial neural network is trained using recorded traffic scenarios, wherein the recorded traffic scenarios include several road users and the recorded traffic scenarios are labelled with score values representing an influence of one road user of the several road users by other road users of the several road users,
a respective score value for one road user of the several road users with respect to another road user of the several road users is calculated based on a determination of a deviation between two trajectories of the one road user of the several road users,
wherein one of the two trajectories is a detected real trajectory that the one road user of the several road users actually takes in a respective recorded traffic scenario, and
wherein a second of the two trajectories is a simulated trajectory determined in a simulation and representing a trajectory that the one road user would take in a same traffic scenario if the another road user of the several road users were not present.
For the following reasons, the examiner submits that the above identified additional limitations to not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitation of “wherein the artificial neural network is trained…” the examiner submits that this merely provides additional detail regarding the nature of the neural network. Regarding the additional limitation of “wherein one of the two…” and “wherein a second of the two…” the examiner submits that this merely defines the two trajectories used for the calculation.
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. And as discussed above, the additional limitation of “wherein the artificial neural network is trained…” this merely provides additional detail regarding the nature of the neural network. Regarding the additional limitation of “wherein one of the two…” and “wherein a second of the two…” this merely defines the two trajectories used for the calculation. Hence, the claim is not patent eligible.
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.
Dependent claims 11-14 do not recite any further limitations that cause the claims 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. Claim 11-14 merely provide additional detail regarding how the calculations of claim 10 are performed.
Therefore, claim(s) 10-14 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 17 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention.
Specifically, claim 17 recites: “wherein the predicted influence is used as a heuristic to restrict a search area to at least one relevant road user when an automated vehicle is pathfinding.”
This matches the language from paragraph [0014] of the specification: “the predicted influence is used as a heuristic to restrict a search area to at least one relevant road user when an automated vehicle is pathfinding.”
The specification provides no further explanation of what this means. It does not discuss pathfinding, a search area, or what it means to “restrict a search area to at least one relevant road user,” anywhere else in the specification.
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 10-16 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Schmidt et al. (US 20200189580 A1) in view of Dingli et al. (US 20210300412 A1).
Regarding claim 10, Schmidt teaches: A method comprising:
predicting an influence of one road user on at least one other road user by evaluating traffic scenarios (See Schmidt [0006] and throughout for determination of influence of maneuver on trajectory of other road users) …
a respective score value for one road user of the several road users with respect to another road user of the several road users is calculated based on a determination of a deviation between two trajectories of the one road user of the several road users,
wherein one of the two trajectories is a [detected real] trajectory that the one road user of the several road users actually takes in a respective recorded traffic scenario, and
wherein a second of the two trajectories is a simulated trajectory determined in a simulation and representing a trajectory that the one road user would take in a same traffic scenario if the another road user of the several road users were not present. (See Schmidt [0006]-[0011] for comparison of “undisturbed predicted trajectory” with predicted trajectories in response to possible driving maneuvers and determination of a value of a characteristic variable (score) based on a function of the deviation of the road user from the predicted trajectory.)
Schmidt does not explicitly teach:
…using a trained artificial neural network,
wherein the artificial neural network is trained using recorded traffic scenarios, wherein the recorded traffic scenarios include several road users and the recorded traffic scenarios are labelled with score values representing an influence of one road user of the several road users by other road users of the several road users,
…
wherein one of the two trajectories is a detected, real trajectory…
However, the use of neural networks to perform real-time data analysis for vehicle control, and the training of such networks, is well known in the art.
Dingli teaches a method of training a neural network for vehicle control based on a predicted and observed influence of a driving maneuver on the trajectory of another vehicle (See Dingli [0013] and throughout for predicting the trajectory of another object, predicting how the other object’s trajectory will change based on the selected trajectory of the host vehicle, selecting a host vehicle trajectory based on the predictions, then comparing the predicted change in trajectory with the actual, measured change in trajectory, and updating the model based on the difference between the predicted trajectory and the actual trajectory.)
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the application, to modify the method of Schmidt to incorporate a trained neural network to perform the determination of influence, and to train that model based on a comparison of predicted and real data as taught in Dingli, in order to provide a more accurate and self-improving calculation.
Regarding claim 11, modified Schmidt teaches: The method of claim 10, wherein the deviation between the two trajectories is determined by an average displacement error or a final displacement error. (See Dingli [0016], [0042] for weighted average of the predicted change and actual change)
Regarding claim 12, modified Schmidt teaches: The method of claim 10, wherein an influence of the one road user of the several road users on exactly one other road user of the several road users is determined from the score value of the one road user of the several road users. (See Schmidt Fig. 1 and [0040]-[0043] for solid line 26 and corresponding lines 30, in which only vehicle 24’s trajectory is changed and therefore the characteristic variable will be based only on the influence of vehicle 10 upon vehicle 24, or dashed line 28 and corresponding lines 32, in which only the trajectory of vehicle 2 is changed and the characteristic variable will be calculated based on the change in vehicle 2’s trajectory in response to vehicle 10.)
Regarding claim 13, modified Schmidt teaches: The method of claim 10, wherein an influence of the one road user of the several road users on all other road users of the several road users in a respective traffic scene is determined from the score value of the one road user of the several road users. (See Schmidt [0067]-[0075] for cost function based on deviation of other vehicles due to predicted maneuver)
Regarding claim 14, modified Schmidt teaches: The method of claim 10, wherein the trained artificial neural network employs
a map-free approach that uses dynamic information about the other road users of the several road users as input information, or
a scene graph that uses all available information, including information about a static infrastructure from a map. (See Dingli [0037] for selection of trajectory based on map data, geometry of vehicle, road conditions, traffic conditions. See [0031] and throughout for detection of road signs and traffic lights.)
Regarding claim 15, Schmidt teaches: A method for operating a vehicle, the method comprising:
predicting an influence of one road user on at least one other road user by evaluating traffic scenarios (See Schmidt [0006] and throughout for determination of influence of maneuver on trajectory of other road users) … and
using the predicted influence to perform a function of the vehicle, (See Schmidt [0013] and throughout for selection of a driving maneuver based on the characteristic value and control of the vehicle according to the selected maneuver.)
…
a respective score value for one road user of the several road users with respect to another road user of the several road users is calculated based on a determination of a deviation between two trajectories of the one road user of the several road users,
wherein one of the two trajectories is a [detected real] trajectory that the one road user of the several road users actually takes in a respective recorded traffic scenario, and
wherein a second of the two trajectories is a simulated trajectory determined in a simulation and representing a trajectory that the one road user would take in a same traffic scenario if the another road user of the several road users were not present. (See Schmidt [0006]-[0011] for comparison of “undisturbed predicted trajectory” with predicted trajectories in response to possible driving maneuvers and determination of a value of a characteristic variable (score) based on a function of the deviation of the road user from the predicted trajectory.)
Schmidt does not explicitly teach:
…using a trained artificial neural network,
wherein the artificial neural network is trained using recorded traffic scenarios, wherein the recorded traffic scenarios include several road users and the recorded traffic scenarios are labelled with score values representing an influence of one road user of the several road users by other road users of the several road users,
…
wherein one of the two trajectories is a detected, real trajectory…
However, the use of neural networks to perform real-time data analysis for vehicle control, and the training of such networks, is well known in the art.
Dingli teaches a method of training a neural network for vehicle control based on a predicted and observed influence of a driving maneuver on the trajectory of another vehicle (See Dingli [0013] and throughout for predicting the trajectory of another object, predicting how the other object’s trajectory will change based on the selected trajectory of the host vehicle, selecting a host vehicle trajectory based on the predictions, then comparing the predicted change in trajectory with the actual, measured change in trajectory, and updating the model based on the difference between the predicted trajectory and the actual trajectory.)
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the application, to modify the method of Schmidt to incorporate a trained neural network to perform the determination of influence, and to train that model based on a comparison of predicted and real data as taught in Dingli, in order to provide a more accurate and self-improving calculation.
Regarding claim 16, modified Schmidt teaches: wherein a probability of a collision occurring between an ego vehicle and a circumjacent road user is determined using the predicted influence of the circumjacent road user with respect to the ego vehicle by a collision warning or collision avoidance system of the ego vehicle. (See Dingli [0046], [0065], for selection of maneuver or driving action based on likelihood of avoiding collision)
Regarding claim 18, modified Schmidt teaches: The method of claim 15, wherein the predicted influence is used as input parameter of a trajectory prediction approach and a level of interaction between pairs of road users is modelled. (See Dingli [0003] where a trajectory is selected, trajectories of other objects are predicted based on the selected trajectory, and the selected trajectory is adjusted based on the predicted trajectory.)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JACOB KENT BESTEMAN-STREET whose telephone number is (571)272-2501. The examiner can normally be reached M-TH 8:00-5:00. 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, Peter Nolan can be reached on 571-270-7016. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format.
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/JACOB KENT BESTEMAN-STREET/
Examiner, Art Unit 3661
/PETER D NOLAN/Supervisory Patent Examiner, Art Unit 3661
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