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 .
Amendments
This action is in response to amendments filed November 25th, 2025, in which Claims 1, 6, 8, 12, 14, and 15 are amended. Claims 16-19 are added. The amendments have been entered, and Claims 1-19, of which Claims 1, 14, 15, and 19 are independent, are currently pending.
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.
Claims 1-13 and 15-19 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.
Independent Claims 1, 15, and 19 each recite the term simple which is a relative term which renders the claim indefinite. The term simple 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. For the purpose of examination, the claims will be interpreted as if the word simple were omitted.
Dependent claims are rejected for inheriting the indefiniteness of their parent claim.
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 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites a method¸ thus a process, one of the four statutory categories of patentable subject matter. However, the claim further recites transforming [a[ first data set into a second data set having at least two classes of a logical scenario representing a vehicle analysis, said transforming comprising flattening the sensor data, turning the sensor data into a time series sequence of constant slopes or other simple mathematical expressions, devoid of random fluctuations (a mental process of analyzing and altering information) and using an algorithm on the second data set to reduce the complexity of the logical scenario (a mental process of analyzing and altering information, as algorithm is broadly recited), and minimizing a number of classes in the time series sequence in order to reduce the complexity of the logical scenario (a mental process of changing labels) . Thus, the claim recites an abstract idea of modifying a dataset in two steps in order to reduce complexity of the data.
The claim does not recite any additional elements which could integrate the abstract idea into a practical application, because the additional elements consist of:
providing a first set of sensor data of a trip of an ego vehicle recorded by at least one on-board sensor, which is insignificant extra-solution activity of data gathering, necessary for all uses of the abstract idea (see MPEP 2106.05(g));
the first data set comprising a temporal sequence of distinct vehicle actions, each distinct vehicle action belonging to a certain class, said class defining a vehicle action taking place over a limited time interval, which merely describes the data being operated on, thus specifying the field of use (see MPEP 2106.05(h)); and
outputting a third set of data representing a reduced complexity logical scenario of the second data set, which is insignificant extra-solution activity of data output or display, necessary for all uses of the abstract idea (see MPEP 2106.05(g)).
Merely specifying particular data or a field of use, and insignificant extra-solution activity, cannot integrate the abstract idea into a practical application, thus the claim is directed to the abstract idea of modifying a dataset in two steps in order to reduce complexity of the data.
Finally, the additional elements, taken alone and in combination, cannot provide significantly more than the abstract ideas because they are instances of transmitting particular data over a network, which is well-understood, routine, and conventional (see MPEP 2106.05(d) and (h)) and have no nexus between them which could provide an inventive concept. Thus, the claim is ineligible.
Claims 2-4, 10, and 11, each dependent upon Claim 1, each recite additional steps of the abstract idea (Claim 2: minimize a number of classes or maximize a degree of an agreement; Claim 3: selecting, extracting, or classifying a change of features; Claim 4: to modify at least one value; Claim 10: calculate whether the logical scenario meets a criterion; Claim 11: calculate a deviation and terminate optimization by the algorithm) but no new additional elements, thus no additional elements which could integrate the abstract idea into a practical application nor provide significantly more than the abstract idea itself.
Claims 5-9, 12, 13, and 16-18, each dependent upon Claim 1, recite only additional elements which specify the particular technological environment in which the abstract idea is to be performed, including the type of data to be analyzed, (Claim 5: the classes include …; Claim 6: the values contained by the classes are …; Claim 7: the location-related data are …; Claim 8: the location-related data are; Claim 9: the algorithm is used for a number of cycles; Claim 12: the calculation is carried out at regular intervals; Claim 13: the algorithm is a machine learning algorithm, etc.; Claim 16: the time-related data includes …; Claim 17: the location related actions include …; Claim 18; the logical scenario comprises …) which can neither integrate the abstract idea into a practical application nor provide significantly more than the abstract idea itself (see MPEP 2106.05(h)).
Claim 14 recites a method¸ thus a process, one of the four statutory categories of patentable subject matter. However, the claim further recites an optimization algorithm that calculates an extreme value of a loss function for generating the reduced complexity logical representation of [a] first training data set (a mathematical process). Thus, the claim recites an abstract idea of optimizing a loss function to achieve reduce complexity of data.
The claim does not recite any additional elements which could integrate the abstract idea into a practical application, because the additional elements consist of:
receiving a first training dataset having at least two classes of a logical scenario representing a vehicle action and receiving a second training data set representing a reduced complexity logical scenario of the first training data set, which are both insignificant extra-solution activity of data gathering, necessary for all uses of the abstract idea (see MPEP 2106.05(g)); and
training the machine learning algorithm by performing the mathematical concept, which merely recites the expected outcome of the abstract idea (see MPEP 2106.05(f)(1)) without details of how the solution is to be accomplished.
Insignificant extra-solution activity nor merely reciting the idea of a solution or outcome (“apply it”) cannot integrate the abstract idea into a practical application, thus the claim is directed to the abstract idea of optimizing a loss function to achieve reduce complexity of data.
Finally, the additional elements, taken alone and in combination, cannot provide significantly more than the abstract ideas because a) are instances of transmitting data over a network, which is well-understood, routine, and conventional (see MPEP 2106.05(d)) and b) mere instructions to apply an exception cannot provide an inventive concept (see MPEP 2106.05(f)) and the additional elements have no nexus between them which could provide an inventive concept. Thus, the claim is ineligible.
Claim 15 recites a system comprising: at least one on-board sensor, thus an apparatus, one of the four statutory categories of patentable subject matter. However, the claim only recites a system comprising components (a transformer¸ a control unit) to perform precisely the steps of the method of Claim 1. As performance of an abstract idea on generic computer components can neither integrate an abstract idea into a practical application nor provide significantly more than the abstract idea itself (see MPEP 2106.05(f)(2)), Claim 15 is rejected for reasons set forth in the rejection of Claim 1.
Claim 19 recites precisely the method of Claim 1, with the additional limitation of creating test scenarios with the intended use for simulations for verifying or validating driver assistance systems. As creating test scenarios is simply an mental process step (merely generating or categorizing the scenario data), Claim 19 is rejected for reasons set forth in the rejection of Claim 1.
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.
(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.
Claims 14 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by Krajewski et al., “Data-Driven Maneuver Modeling using Generative Adversarial Networks and Variational Autoencoders for Safety Validation of Highly Automated Vehicles” (as provided by the applicant in the Information Disclosure Statement dated 4/3/2023)
Regarding Claim 14, Krajewski teaches a method for providing a trained machine learning algorithm for generating a reduced complexity representation of a data set of sensor data (Krajewski, pg. 2386, Fig. 4, “The encoder creates a latent representation of the input” where the input “trajectories” is sensor data and a “latent code” is a reduced complexity representation), the method comprising: receiving a first data set having at least two classes of a logical scenario representing a vehicle action, the first training data set comprising a temporal sequence of distinct vehicle actions, each distinct vehicle action belonging to a certain class, said class defining a vehicle action taking place over a limited time interval (Krajewski, pg. 2386, 2nd column, first paragraph, “the HighD dataset not only provides the raw vehicle tracks, but also annotations for all lane changes. To create these, an algorithm detects vehicles crossing lane markings. All time samples before and after these crossing have been labeled as a lane change if the latitudinal movement is above a specified threshold. For every lane change, the trajectory of the according vehicle and meta information, like the duration or the direction of the lane change, are given” where classes include existence and direction of lane changes); receiving a second training dataset representing a reduced complexity logical scenario of the first training dataset, wherein the complexity of the logical scenario is reduced by minimizing a number of classes in the time series sequence (Krajewski, pg. 2386, 2nd column, 1st paragraph, “we mirror all lane changes that are facing to the left, so only lane changes to the right lane are used in the following”); training the machine learning algorithm by an optimization algorithm that calculates an extreme value of a loss function for generating the reduced complexity logical representation of the first training data set (Krajewski, pg. 2386, Fig. 4, “The loss of the network is defined as a combination of the reconstruction error and the Kullback-Leibler (KL) divergence” & pg. 2387, 2nd column, 2nd paragraph, “minimizes the mean square error loss” & 4th paragraph, “we use the Adam optimizer” where reduced complexity logical representation is the latent codes for only lane changes to the right hand lane).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-8, 10-13, and 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Krajewski et al., “Data-Driven Maneuver Modeling using Generative Adversarial Networks and Variational Autoencoders for Safety Validation of Highly Automated Vehicles” (as provided by the applicant in the Information Disclosure Statement dated 4/3/2023), in view of Thiemann et al., “Estimating Acceleration and Lane-Changing Dynamics from Next Generation Simulation Trajectory Data.”
Regarding Claim 1, Krajewski teaches a method for generating a reduced complexity logical representation of a dataset of sensor data (Krajewski, pg. 2386, Fig. 4, “The encoder creates a latent representation of the input” where the input “trajectories” is sensor data and a “latent code” is a reduced complexity logical representation), the method comprising: providing a first data set of sensor data of a trip of an ego vehicle (Krajewski, pg. 2383, Fig. 1, “Our proposed methods use real trajectories from the HighD dataset to train neural networks”) recorded by at least one on-board sensor (Krajewski, pg. 2384, 1st column, 2nd paragraph, & Fig. 2, “a recent dataset of vehicle trajectories on German highways extracted from drone recordings” where the video camera is on-board the drone, where the video is a first dataset) the first training data set comprising a temporal sequence of distinct vehicle actions, each distinct vehicle action belonging to a certain class, said class defining a vehicle action taking place over a limited time interval (Krajewski, pg. 2386, 2nd column, first paragraph, “the HighD dataset not only provides the raw vehicle tracks, but also annotations for all lane changes. To create these, an algorithm detects vehicles crossing lane markings. All time samples before and after these crossing have been labeled as a lane change if the latitudinal movement is above a specified threshold. For every lane change, the trajectory of the according vehicle and meta information, like the duration or the direction of the lane change, are given” where classes include existence and direction of lane changes); … wherein the complexity of the logical scenario is reduced by minimizing a number of classes in the time series sequence (Krajewski, pg. 2386, 2nd column, 1st paragraph, “we mirror all lane changes that are facing to the left, so only lane changes to the right lane are used in the following”) … using an algorithm on the … data set to reduce the complexity of the logical scenario …; and outputting a third data set representing a reduced complexity logical scenario of the second dataset (Krajewski, pg. 2386, Fig. 4, “The encoder creates a latent representation of the input” where a “latent code” is a dataset representing a reduced complexity logical scenario & “the decoder reconstructs the input from the latent representation”).
Krajewski does not teach transforming the first data set into a second data set, said transforming comprising flattening the sensor data from the first data set, turning the sensor data into a time series sequence of constant slopes or other simple mathematical expressions, devoid of random fluctuations, but Thiemann, also in the field of automotive trajectory data, teaches these limitations.
Specifically, Thiemann teaches transforming the first data set (i.e. a first data set of trajectory data) into a second data set having at least two classes of logical scenario representing a vehicle action (the data set still includes lane changes and not lane changes), said transforming comprising flattening the sensor data from the first data … devoid of random fluctuations (Thiemann, Abstract, “a smoothing algorithm is proposed for positions, velocities, and accelerations” see pg. 93, Fig. 2) turning the sensor data into a time series sequence of constant slopes or other simple mathematical expressions (Thiemann, Abstract, “The Next Generation Simulation (NGSIM) trajectory data sets provide longitudinal and lateral position information … Velocity and acceleration information cannot be extracted directly [but] … is obtained in this way [i.e. the smoothing algorithm” where the velocity time series is a time series sequence of constant slopes because velocity is the slope of position).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to performing smoothing on trajectory data, such as that of Krajewski, using the methods of Thiemann, to both remove random fluctuations and to obtain velocity and acceleration data, before learning latent codes. The motivation to do so is that a) noisy unsmooth data causes issues in data analysis (Thiemann, pg. 91, Fig. 1) and that b) velocity information is also important in trajectory data analysis (Thiemann, pg. 90, 2nd column, 2nd-to-last paragraph, “investigation into topics for which velocities and accelerations play a significant role, such as testing or calibrating car-following models”).
Regarding Claim 2, the Krajewski/Thiemann combination of Claim 1 teaches the method according to Claim 1 (and thus the rejection of Claim 1 is incorporated). Krajewski further teaches wherein the algorithm minimizes a number of classes representing a vehicle action (Krajewski, pg. 2386, 2nd column, 1st paragraph, “we mirror all lane changes that are facing to the left, so only lane changes to the right lane are used in the following”) and/or maximizes a degree of an agreement of the reduced complexity logical scenario of the third data set with the logical scenario of the second data set (Krajewski, pg. 2385, 2nd column, 3rd paragraph, “by training the network to minimize the difference between the network input and output, the latent space representation has to retain as much information as possible”).
Regarding Claim 3, the Krajewski/Thiemann combination of Claim 1 teaches the method according to Claim 1 (and thus the rejection of Claim 1 is incorporated). The combination as described, via Thiemann, further teaches wherein the transforming of the first data set into the second dataset having the at least two classes of the logical scenario representing a vehicle action comprises selecting, extracting, or classifying of change of features of the first data set representing a vehicle state (Thiemann, computing the velocity requires selecting and extracting the changing position features, see Thiemann, pg. 91, “Extracting Velocity and Acceleration Information”).
Regarding Claim 4, the Krajewski/Thiemann combination of Claim 1 teaches the method according to Claim 1 (and thus the rejection of Claim 1 is incorporated). Krajewski further teaches wherein the algorithm is equipped to modify at least one value, one number (Krajewski, pg. 2386, Fig. 4, the values/numbers in the trajectory are modified into latent code values), and/or one type of the multiplicity of classes representing a vehicle action (Krajewski, pg. 2386, 2nd column, 1st paragraph, “we mirror all lane changes that are facing to the left, so only lane changes to the right lane are used in the following”).
Regarding Claim 5, the Krajewski/Thiemann combination of Claim 1 teaches the method according to Claim 1 (and thus the rejection of Claim 1 is incorporated). Krajewski further teaches wherein the at least two classes representing a vehicle action includes at least one value of … a change in direction and/or lane (Krajewski, pg. 2386, 2nd column, first paragraph, “the HighD dataset not only provides the raw vehicle tracks, but also annotations for all lane changes”).
Regarding Claim 6, the Krajewski/Thiemann combination of Claim 1 teaches the method according to Claim 1 (and thus the rejection of Claim 1 is incorporated). Krajewski further teaches wherein (Krajewski, pg. 2386, 1st column, last paragraph, “All time samples before and after these crossing have been labeled as a lane change if the latitudinal movement is above a specified threshold”) and/or location related data (Krajewski, Abstract, “trajectories”).
Regarding Claim 7, the Krajewski/Thiemann combination of Claim 6 teaches the method according to Claim 6 (and thus the rejection of Claim 6 is incorporated). Krajewski further teaches wherein the location-related data are relative data of the ego vehicle with reference to … fixed objects or a distance to the ego vehicle from … fixed objects (Krajewski, pg. 2386, 2nd column, 1st paragraph, “we transform the coordinate system of the trajectories so that …
y
=
0
at the crossed lane marking” where y is a distance from the fixed object lane marking, also see Fig. 6).
Regarding Claim 8, the Krajewski/Thiemann combination of Claim 7 teaches the method according to Claim 7 (and thus the rejection of Claim 7 is incorporated). Krajewski further teaches wherein the location-related data are location-related actions (Krajewski, Abstract, “trajectories” are movements/actions of the vehicle).
Regarding Claim 10, the Krajewski/Thiemann combination of Claim 1 teaches the method according to Claim 1 (and thus the rejection of Claim 1 is incorporated). Krajewski further teaches wherein it is calculated whether the logical scenario represented by the third data set meets a predefined exclusion criterion (Krajewski, pg. 2386, Fig. 5, “The discriminator learns to distinguish synthetic from given real trajectories” where deciding real vs fake is calculated whether the scenario meets a predefined exclusion criterion).
Regarding Claim 11, the Krajewski/Thiemann combination of Claim 10 teaches the method according to Claim 10 (and thus the rejection of Claim 10 is incorporated). Krajewski further teaches wherein a deviation of the third data set from the second data set is calculated (Krajewski, pg. 2386, Fig. 4, “The loss of the network is defined as a combination of the reconstruction error and the Kullback-Leibler (KL) divergence” or pg. 2386, Fig. 5, “The discriminator learns to distinguish synthetic from given real trajectories”), and wherein further optimization of the third data set by the algorithm is terminated (Krajewski, pg. 2385, 2nd column, 3rd paragraph, “After training” denotes that the optimization has been terminated at some point) or a third dataset last output by the algorithm is discarded if the deviation … causes the exclusion criterion to be met (Krajewski, pg. 2386, Fig. 5, “The discriminator learns to distinguish synthetic from given real trajectories” where fake trajectories are no longer used/discarded).
Regarding Claim 12, the Krajewski/Thiemann combination of Claim 11 teaches the method according to Claim 11 (and thus the rejection of Claim 11 is incorporated). Krajewski further teaches wherein the calculation of the deviation of the third data set from the second data set is carried out at predetermined intervals and/or at the end of a specified optimization cycle (Krajewski, pg. 2387, 2nd column, 2nd paragraph, “the network already minimizes the mean squared error loss between the original trajectories and the reconstructed trajectories during training” where training, e.g. pg. 2387, 2nd column, 4th paragraph, “we use the Adam optimizer” teaches optimization loops/cycles with batches/predetermined intervals of training data, see pg. 2387, Tables I & IV, “BS: batch size”).
Regarding Claim 13, the Krajewski/Thiemann combination of Claim 1 teaches the method according to Claim 1 (and thus the rejection of Claim 1 is incorporated). Krajewski further teaches wherein the algorithm is a machine learning algorithm, and artificial neural network, a greedy algorithm, or a hill climbing algorithm (Krajewski, pg. 2387, 2nd column, 4th paragraph, “we use the Adam optimizer” where Adam is a greedy hill climbing algorithm & pg. 2386, Fig. 4, a “Variational Autoencoder” is a machine learning network and an artificial neural network).
Claim 15 recites a system … comprising the sensor and (as interpreted under 35 U.S.C. 112(f)) generic computer components to perform precisely the method of Claim 1. As Krajewski teaches the drone camera sensor (Krajewski, pg. 2384, Fig. 2) and performs their method on a computer (Krajewski, pg. 2387, 2nd column, 4th paragraph, “we use the Adam optimizer”), Claim 15 is rejected for reasons set forth in the rejection of Claim 1.
Regarding Claim 16, the Krajewski/Thiemann combination of Claim 6 teaches the method according to Claim 6 (and thus the rejection of Claim 6 is incorporated). The combination has already been shown to teach, via Thiemann’s computation of acceleration, wherein the time-related data includes a duration of a longitudinal and/or transverse acceleration of the ego vehicle (Thiemann, pg. 91, 1st column, “Extracting velocity and acceleration information”).
Regarding Claim 17, the Krajewski/Thiemann combination of Claim 8 teaches the method according to Claim 8 (and thus the rejection of Claim 8 is incorporated). Krajewski further teaches wherein the location related actions include a start of a vehicle action at a first geographical point [and] an end of the vehicle action at a second geographical point (Krajewski, pg. 2386, Fig. 6, the trajectories have start and end points at different positions/geographical points).
Regarding Claim 18, the Krajewski/Thiemann combination of Claim 1 teaches the method according to Claim 1 (and thus the rejection of Claim 1 is incorporated). Krajewski further teaches wherein said logical scenario comprises a scenario that can be modified by altering [a] variable value (any of the position values in the scenario can be altered).
Regarding Claim 19, Krajewski teaches a method comprising: generating a reduced complexity logical representation of a dataset of sensor data (Krajewski, pg. 2386, Fig. 4, “The encoder creates a latent representation of the input” where the input “trajectories” is sensor data and a “latent code” is a reduced complexity logical representation), said generating comprising: providing a first data set of sensor data of a trip of an ego vehicle (Krajewski, pg. 2383, Fig. 1, “Our proposed methods use real trajectories from the HighD dataset to train neural networks”) recorded by at least one on-board sensor (Krajewski, pg. 2384, 1st column, 2nd paragraph, & Fig. 2, “a recent dataset of vehicle trajectories on German highways extracted from drone recordings” where the video camera is on-board the drone, where the video is a first dataset) the first training data set comprising a temporal sequence of distinct vehicle actions, each distinct vehicle action belonging to a certain class, said class defining a vehicle action taking place over a limited time interval (Krajewski, pg. 2386, 2nd column, first paragraph, “the HighD dataset not only provides the raw vehicle tracks, but also annotations for all lane changes. To create these, an algorithm detects vehicles crossing lane markings. All time samples before and after these crossing have been labeled as a lane change if the latitudinal movement is above a specified threshold. For every lane change, the trajectory of the according vehicle and meta information, like the duration or the direction of the lane change, are given” where classes include existence and direction of lane changes); … wherein the complexity of the logical scenario is reduced by minimizing a number of classes in the time series sequence (Krajewski, pg. 2386, 2nd column, 1st paragraph, “we mirror all lane changes that are facing to the left, so only lane changes to the right lane are used in the following”) … using an algorithm on the … data set to reduce the complexity of the logical scenario …; and outputting a third data set representing a reduced complexity logical scenario of the second dataset (Krajewski, pg. 2386, Fig. 4, “The encoder creates a latent representation of the input” where a “latent code” is a dataset representing a reduced complexity logical scenario & “the decoder reconstructs the input from the latent representation”); and creating test scenarios for simulations for verifying or validating driver assistance systems (Krajewski, Abstract, “Scenario-based validation is a promising approach for safety validation of highly automated driving systems. By modeling relevant driving scenarios, utilization simulations and selecting insightful test cases, the testing effort is reduced”).
Krajewski does not teach transforming the first data set into a second data set, said transforming comprising flattening the sensor data from the first data set, turning the sensor data into a time series sequence of constant slopes or other simple mathematical expressions, devoid of random fluctuations, but Thiemann, also in the field of automotive trajectory data, teaches these limitations.
Specifically, Thiemann teaches transforming the first data set (i.e. a first data set of trajectory data) into a second data set having at least two classes of logical scenario representing a vehicle action (the data set still includes lane changes and not lane changes), said transforming comprising flattening the sensor data from the first data … devoid of random fluctuations (Thiemann, Abstract, “a smoothing algorithm is proposed for positions, velocities, and accelerations” see pg. 93, Fig. 2) turning the sensor data into a time series sequence of constant slopes or other simple mathematical expressions (Thiemann, Abstract, “The Next Generation Simulation (NGSIM) trajectory data sets provide longitudinal and lateral position information … Velocity and acceleration information cannot be extracted directly [but] … is obtained in this way [i.e. the smoothing algorithm” where the velocity time series is a time series sequence of constant slopes because velocity is the slope of position).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to performing smoothing on trajectory data, such as that of Krajewski, using the methods of Thiemann, to both remove random fluctuations and to obtain velocity and acceleration data, before learning latent codes. The motivation to do so is that a) noisy unsmooth data causes issues in data analysis (Thiemann, pg. 91, Fig. 1) and that b) velocity information is also important in trajectory data analysis (Thiemann, pg. 90, 2nd column, 2nd-to-last paragraph, “investigation into topics for which velocities and accelerations play a significant role, such as testing or calibrating car-following models”).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Krajewski, in view of Thiemann, and further in view of Aliper, US PG Pub 2020/0090049.
Regarding Claim 9, the Krajewski/Thiemann combination of Claim 1 teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Krajewski further teaches wherein the algorithm is used on the third dataset output by the algorithm (Krajewski, pg. 2386, Figs. 4 & 5, the latent code goes back into the neural network algorithm). Krajewski does not teach, but Aliper teaches, wherein the algorithm is used for a predetermined number of optimization cycles (Aliper, [0130], “The VAE-TTLP model can be trained … Training is terminated when the model loss converges or a maximum number of iterations is reached, which can be defined”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to train the VAE/use the algorithm in training in Krajewski only until a maximum number of iterations is reached, as does Aliper. The motivation to do so is to make sure that training processing of the VAE does indeed complete and does not run exceedingly long.
Response to Arguments
Applicant’s arguments filed November 24th, 2025 have been fully considered, but are not fully persuasive.
Applicant’s amendments have corrected the issues causing Claim Objections and 35 U.S.C. 112(b) rejections in the previous claim set, and the objections and relevant rejections have been withdrawn. However, the amendments have required new 35 U.S.C. 112(b) rejections to be presented.
Applicant’s arguments regarding the 35 U.S.C. 101 rejections of the claims have been fully considered, but are unpersuasive.
Applicant argues “one consideration that is indicative of integration of an exception into a practical application is ‘an additional element reflects … an improvement to other technology or technical field” arguing that “what is being improved are driver assistance systems.” However, no such additional element is recited in the claim language. Claims 1-18 do not recite any driver assistance system at all; Claim 19 only recites the driver’s assistance system as an intended use of the created scenario. The claim must recite an additional element which performs the integration of the abstract idea into the recited practical application. Applicant’s own response states “The claimed invention provides a method for generating a reduced complexity logical representation of a dataset” – mere manipulation of data, performable (as it is recited broadly) in the human mind, is not patent eligible subject matter. The only additional elements of independent Claim 1 are receiving the data, outputting the data, and specifying the particular kind of data that is to be manipulated – none of which can integrate an abstract idea into a practical application nor provide significantly more than the abstract idea itself.
Applicant’s argument regarding the prior art rejections of the previous office action have been fully considered, and are alternatively unpersuasive and moot.
Applicant’s assertion that Krajewski fails to teach said transforming comprising flattening the sensor data from the first data set, turning the sensor data into a time series sequence of constant slopes or other simple mathematical expressions, devoid of random fluctuations is moot, because new reference Thiemann is relied upon to teach this limitation, via smoothing the position noisy data (i.e. flattening/devoid of random fluctuations) and generating velocity and acceleration data (turning the sensor data into a time series sequence of constant slopes).
Applicant’s assertion that “in the context of the claimed invention, a ‘logical scenario’ is a time series of at least one variable characterizing motion of a virtual vehicle, the time series being set up as a temporal sequence of elementary driving maneuvers” has been considered; however, this assertion is not supported by the broadest reasonable interpretation of the claims, in light of the specification. Further, in the current rejection, the claimed second data set is now identified as the smooth trajectory data (produced by Thiemann), which is flattened and comprised of a time series of slopes.
Applicant argues “with definitions of ‘class’ and ‘logical scenario” in the claimed invention, Krajewski fails to teach or suggest transforming the first data set into a second data set having at least two classes of a logical scenario representing a vehicle action.” However, the second data set clearly has two classes, e.g. lane-changing and not-lane-changing, in its trajectory data. Neither “logical scenario” nor “class” are defined in the specification, and the combination described in the rejection fulfills all claim limitations.
Applicant argues that “Krajewski fails to teach or suggest outputting a third data set representing a reduced complexity logical scenario of the second data set.” However, the latent codes clearly represent a reduced complexity version of the same input trajectories describing lane changes, therefore we have a third data set representing a reduced complexity logical scenario of the second dataset.
Applicant’ arguments regarding the dependent claims rely upon features argued with respect to independent Claim 1, and are thus also unpersuasive.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Danna, US PG Pub 2022/0198096, also teaches scenario generation for assessing driver assistance/autonomous vehicle systems.
Applicant’s amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/BRIAN M SMITH/Primary Examiner, Art Unit 2122