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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant’s submission filed on May 22nd, 2026 has been entered.
Amendments
This action is in response to amendments filed May 22nd, 2026, in which Claims 1, 10, 14, 15, and 19 are amended. Claims 20-22 are added. The amendments have been entered, and Claims 1-22, of which Claims 1, 14, 15, and 19 are independent, are currently pending.
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 14 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 written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Applicant’s amendment add a step of transforming the first data set into a second data set having at least two classes of a logical scenario … said transforming comprising turning the sensor data into a time series sequence of constant slopes … However, this step appears to be described in [0064-0066] of the specification, regarding datasets DS1 into dataset DS2. The “first data set” of Claim 14, however, is dataset S1’ of training dataset TD1 (paragraphs [0080-0084]). There is no indication anywhere in the specification that any training data receives the recited transformation (which would be inconsistent with the purpose training the machine learning model to generate scenarios, as recited in Claim 14). Applicant appears to have taken features from two distinct embodiments in currently-recited Claim 14, features which do not appear to exist together in any single embodiment.
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 1-22 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, 14, 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.
Independent Claims 1, 14, 15, and 19 each recite the term random in the term devoid of random fluctuations. The terms random and fluctuations appear nowhere in the originally filed disclosure; however, the limitation appears to be supported because it does not disclose any randomness being introduced in generating the “fluctuations”/changes in the scenario (e.g. of Fig.1). However, as implied by the applicant’s remarks regarding the term random on pg. 14 of the response, random appears to be intended to be interpreted as a relative term of degree, where “devoid of random fluctuations” is intended to mean “not too bumpy-looking” (see the Figure on pg. 14 and the discussion of “chaotic systems” – as deterministic chaotic systems, such as a double pendulum, while “bumpy-looking” are not random in any sense of a random variable). Therefore, it is indefinite as to what the scope of random and of devoid of random fluctuations actually entails. For the purpose of examination, as long as no randomness is involved in an algorithm for transforming the sensor data, the sensor data will be interpreted as being devoid of random fluctuations.
Independent Claims 1, 14, 15, and 19 each recite the term optimal which, devoid of any recited optimization target, is a relative term which renders the claim indefinite. The term optimal 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 optimal were omitted.
Claim 21 recites the limitation the longitudinal and/or transverse acceleration of the ego vehicle, which lacks proper antecedent basis in the claims.
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 1-22 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 including a time series of a variable representing a vehicle action, said transforming comprising 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), 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), and obtaining an optimal abstraction and generalization of the sensor data (a mental process of analyzing and altering information). Thus, the claim recites an abstract idea of modifying a dataset in 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 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, 16-18, and 20-22, 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 …; Claim 20: the two classes include …; Claim 21: the values are a distance; Claim 22: the variable is a velocity) 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); transforming the first data set into a second data set having at least two classes of a logical scenario including a time series of a variable representing a vehicle action, said transforming comprising 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 obtaining an optimal abstraction and generalization of the sensor data (a mental process of analyzing and altering information). Thus, the claim recites an abstract idea of optimizing a loss function to achieve reduce complexity of data including transforming the data to abstract 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:
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 including transforming the data to abstract the 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 1-8 and 13-22 are 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 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); transforming the first data set into a second data set having at least two classes of a logical scenario including a time series of a variable representing a vehicle action, said transforming comprising turning the sensor data into a time series sequence of constant slopes or other simple mathematical expressions, devoid of random fluctuations (Krajewski, pg. 2386, 1st column, last paragraph, “The trajectory itself consists of longitudinal and lateral positions, from which the speeds at every time step can be derived” where speed at different timesteps is a time series sequence of constant slopes as speed is the slope/derivative of position, and the values are fixed/constant, and there is no randomness in the derivation; the second data set still has at least two classes of lane changes before the subsequent algorithm is applied ) … 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 second data set to reduce the complexity of the logical scenario …; 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 obtaining an optimal abstraction and generalization of the sensor data (Krajewski, pg. 2387, Fig. 7, “trajectories that have been restored from the estimated latent code”).
Regarding Claim 2, Krajewski 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, Krajewski teaches the method according to Claim 1 (and thus the rejection of Claim 1 is incorporated). Krajewski 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 … extracting … features of the first data set representing a vehicle state (Krajewski, pg. 2386, 1st column, last paragraph, “The trajectory itself consists of longitudinal and lateral positions, from which the speeds at every time step can be derived” denotes extracting speed features).
Regarding Claim 4, Krajewski 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, Krajewski 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, Krajewski teaches the method according to Claim 1 (and thus the rejection of Claim 1 is incorporated). Krajewski further teaches wherein values contained by the at least two classes representing a vehicle action are time-related data (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, Krajewski 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, Krajewski 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 13, Krajewski 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).
Regarding Claim 14, Krajewski teaches a method, comprising: 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), said providing 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); transforming the first data set into a second data set having at least two classes of a logical scenario including a time series of a variable representing a vehicle action, said transforming comprising turning the sensor data into a time series sequence of constant slopes or other simple mathematical expressions, devoid of random fluctuations (Krajewski, pg. 2386, 1st column, last paragraph, “The trajectory itself consists of longitudinal and lateral positions, from which the speeds at every time step can be derived” where speed at different timesteps is a time series sequence of constant slopes as speed is the slope/derivative of position, and the values are fixed/constant, and there is no randomness in the derivation; the second data set still has at least two classes of lane changes before the subsequent algorithm is applied ) and obtaining an optimal abstraction and generalization of the sensor data (Krajewski, pg. 2387, Fig. 7, “trajectories that have been restored from the estimated latent code”).
Claim 15 recites a system … comprising the sensor and configured 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, Krajewski teaches the method according to Claim 6 (and thus the rejection of Claim 6 is incorporated). Krajewski further teaches wherein the time-related data includes a duration of a longitudinal and/or transverse acceleration of the ego vehicle (Krajewski, pg. 2388, 2nd column, 2nd paragraph, “the duration of the lane change”).
Regarding Claim 17, Krajewski 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, Krajewski 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); transforming the first data set into a second data set having at least two classes of a logical scenario including a time series of a variable representing a vehicle action, said transforming comprising turning the sensor data into a time series sequence of constant slopes or other simple mathematical expressions, devoid of random fluctuations (Krajewski, pg. 2386, 1st column, last paragraph, “The trajectory itself consists of longitudinal and lateral positions, from which the speeds at every time step can be derived” where speed at different timesteps is a time series sequence of constant slopes as speed is the slope/derivative of position, and the values are fixed/constant, and there is no randomness in the derivation; the second data set still has at least two classes of lane changes before the subsequent algorithm is applied ) … 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 second 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”); 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”) ; and obtaining an optimal abstraction and generalization of the sensor data (Krajewski, pg. 2387, Fig. 7, “trajectories that have been restored from the estimated latent code”).
Regarding Claim 20, Krajewski 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 an acceleration process (lane change involves transverse acceleration, thus position during lane change is at least one value of an acceleration process) [and/or] a trip with constant speed of the ego vehicle (Krajewski, pg. 2385, 1st column, last paragraph – 2nd column, 1st paragraph, “In the longitudinal direction, most vehicles keep or constantly change their speed” where “keep” denotes a trip with constant speed).
Regarding Claim 21, Krajewski teaches the method according to Claim 1 (and thus the rejection of Claim 1 is incorporated). Krajewski further teaches wherein values contained by the at least two classes representing a vehicle action are a distance of the longitudinal and/or transverse acceleration of the ego vehicle (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 at the beginning and the end of a lane change is a distance of the transverse acceleration, also see Fig. 6).
Regarding Claim 22, Krajewski teaches the method according to Claim 1 (and thus the rejection of Claim 1 is incorporated). Krajewski further teaches wherein the variable is a vehicle velocity (Krajewski, pg. 2386, 1st column, last paragraph, “the speeds at every time step can be derived” have been identified as the variable in a time series of a variable representing a vehicle action).
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.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Krajewski, in view of in view of Aliper, US PG Pub 2020/0090049.
Regarding Claim 9, Krajewski 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.
Claims 10-12 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 Tenbrock et al., “The ConScenD Dataset: Concrete Scenarios from the highD Dataset According to ALKS Regulation UNECE R157 in OpenX.”
Regarding Claim 10, Krajewski teaches the method according to Claim 1 (and thus the rejection of Claim 1 is incorporated). Krajewski does not explicitly teach wherein it is calculated whether the logical scenario represented by the third data set meets a predefined exclusion criterion, the predefined exclusion criteria including an occurrence of a traffic accident, a violation of a traffic regulation, and/or an intervention of a driver assistance system. However, Tenbrock (also concerning processing the highD trajectories that Krajewski uses) teaches wherein it is calculated whether the logical scenario represented by the third data set meets a predefined exclusion criterion, the predefined exclusion criteria including … a violation of a traffic regulation (Tenbrock, pg. 4, 1st column, 3rd paragraph, “the database entries are filtered according to the system boundaries of ALKS systems as specified by the UNECE Regulation” that is, the trajectories in the dataset that do not confirm to the UNECE traffic regulations are excluded/filtered out). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to filter out trajectories representing undesired scenarios, i.e. those violating the UNECE Regulations, from the trajectories of Krajewski, as does Tenbrock. The motivation to do so is “to ensure relevance of the derived scenarios” (Tenbrock, pg. 4, 1st column, 3rd paragraph).
Regarding Claim 11, the Krajewski/Tenbrock 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/Tenbrock 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”).
Response to Arguments
Applicant’s arguments filed May 22nd, 2026 have been fully considered, but are not fully persuasive.
Applicant has not made any amendments regarding the 35 U.S.C. 112(b) rejection of the previous office action, regarding simple as a relative term of degree, and thus the rejection has been maintained. Also, additional 35 U.S.C. 112(b) and 35 U.S.C. 112(a) rejections have been identified in this office action.
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 and 20-22 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” and “obtaining an optimal abstraction and generalization of the sensor data” – 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.
Regarding Claim 14, applicant asserts that Krajewski fails to teach said transforming comprising turning the sensor data into a time series sequence of constant slopes or other simple mathematical expressions, devoid of random fluctuations and argues this is so because “in addressing independent Claim1, the Examiner concedes that Krajewski fails to teach or suggest this feature.” However, the rejection of Claim 1 of the previous office action states that Krajewski fails to teach a different limitation – that 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. The current claim language does not require flattening (which Krajewski fails to teach), while Krajewski teaches the remainder of the limitation, as indicated in the current rejection of each of the independent claims.
The remainder of applicant’s arguments are moot, as Thiemann is not relied upon to teach any limitations of any claim.
Applicant’ arguments regarding the dependent claims rely upon features argued with respect to independent Claim 1, and are thus also unpersuasive. Applicant has presented no reasons why Krajewski would fail to teach the features of new Claims 20-22.
Conclusion
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/BRIAN M SMITH/Primary Examiner, Art Unit 2122
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