DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Information Disclosure Statements
The Information Disclosure Statements (IDS) filed on 4/24/2024 has been acknowledged.
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in Germany on 4/4/2023.
Status of Application
Claims 1-17 are pending.
Claims 12-17 have been added.
Claims 1, 3, 7, 8, and 11 have been amended.
Claims 1, 8, and 11 are independent.
This FINAL Office action is in response to the “Amendments and Remarks” received on 2/19/2026.
Response to Arguments/Remarks
With respect to Applicant’s remarks filed on 2/19/2026; Applicant's “Amendments and Remarks” have been fully considered. Applicant’s remarks will be addressed in sequential order as they were presented.
With respect to the Claim objections, applicants “Amendment and Remarks” have been fully considered and are persuasive. The Claim objections have been withdrawn.
With respect to the claim rejections under 35 U.S.C. § 101, applicants “Amendment and Remarks” have been fully considered and were persuasive. Therefore the claim rejections under 35 U.S.C. § 101 have been withdrawn.
With respect to the previous claim rejections under 35 U.S.C. § 102 and § 103, applicant has amended the independent claim and these amendments have changed the scope of the original application and the Office has supplied new grounds for rejection attached below in the FINAL office action and therefore the prior arguments are considered moot.
It is the Office’s stance that all of applicant arguments have been considered and the rejections remain.
Final Office Action
CLAIM INTERPRETATION
During examination, claims are given the broadest reasonable interpretation consistent with the specification and limitations in the specification are not read into the claims. See MPEP §2111, MPEP §2111.01 and In re Yamamoto et al., 222 USPQ 934 10 (Fed. Cir. 1984). Under a broadest reasonable interpretation, words of the claim must be given their plain meaning, unless such meaning is inconsistent with the specification. See MPEP 2111.01 (I). It is further noted it is improper to import claim limitations from the specification, i.e., a particular embodiment appearing in the written description may not be read into a claim when the claim language is broader than the embodiment. See 15 MPEP 2111.01 (II).
A first exception to the prohibition of reading limitations from the specification into the claims is when the Applicant for patent has provided a lexicographic definition for the term. See MPEP §2111.01 (IV). Following a review of the claims in view of the specification herein, the Office has found that Applicant has not provided any lexicographic definitions, either expressly or implicitly, for any claim terms or phrases with any reasonable clarity, deliberateness and precision. Accordingly, the Office concludes that Applicant has not acted as his/her own lexicographer.
A second exception to the prohibition of reading limitations from the specification into the claims is when the claimed feature is written as a means-plus-function. See 35 U.S.C. §112(f) and MPEP §2181-2183. As noted in MPEP §2181, a three prong test is used to determine the scope of a means-plus-function limitation in a claim:
the claim limitation uses the term "means" or "step" or a term used as a substitute for "means" that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function
the term "means" or "step" or the generic placeholder is modified by functional language, typically, but not always linked by the transition word "for" (e.g., "means for") or another linking word or phrase, such as "configured to" or "so that"
the term "means" or "step" or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
The Office has found herein that claims do not contain limitations of means or means type language that must be analyzed under 35 U.S.C. §112 (f).
Claim Objections
Claim 11 has typographical errors that need to be corrected.
Claim 11 states steps in order, a, b, c, d….but appears to be missing step f. The Office was not sure if this was a typo or intentional but this issue suggests missing steps. The Office will interpret step g. as step f.
This Office suggests going through all claims and looking for similar errors as the above listed errors, as the above list was exemplary in nature and by no means exhaustive. Appropriate action is required.
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102(e), (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
Claims 1-6, 8-9, and 11-17are rejected under 35 USC 103 as being unpatentable over Marsillach et al. (United States Patent Publication 2024/0132103) in view of Lin et al. (United States Patent Publication 2023/0054263).
With respect to Claim 1: While Marsillach discloses “A computer-implemented method for predicting at least one trajectory area of at least one participant of a traffic scene” [Marsillach, ¶ 0032-0035 with Figure 2];
“and for operating a vehicle based on the at least one trajectory area” [Marsillach, ¶ 0032-0035 with Figure 2];
“the method comprising the following steps: generating a scene representation of the traffic scene based on aggregated scene-specific information” [Marsillach, ¶ 0032-0035 with Figure 2];
“obtained from at least one sensor of the vehicle over a past observation horizon” [Marsillach, ¶ 0032-0035 with Figure 2];
“wherein the aggregated scene-specific information includes position and movement parameters of the at least one participant relative to a roadway” [Marsillach, ¶ 0032-0035 with Figure 2];
“determining a current position of the participant and a current track section on which the at least one participant is currently located” [Marsillach, ¶ 0032-0035 with Figure 2];
“transforming the scene representation into at least one Frenet coordinate system to provide at least one resulting Frenet representation of the traffic scene” [Marsillach, ¶ 0041-0044 and 0050];
“wherein the current track section specifies at least one section of a respective reference path for the Frenet transformation” [Marsillach, ¶ 0041-0044];
“and wherein the Frenet transformation reduces sensitivity of the resulting Frenet representation to curvature of the roadway” [Marsillach, ¶ 0041-0044 and 0050];
“predicting, using a pretrained artificial-intelligence-based (AI) prediction model” [Marsillach, ¶ 0050-0052];
“at least one future trajectory area of the at least one participant” [Marsillach, ¶ 0050-0052];
“based on the at least one resulting Frenet representation of the traffic scene” [Marsillach, ¶ 0050-0052];
“and using the predicted at least one future trajectory area to adapt operation of at least one vehicle function by a vehicle control or driver assistance system” [Marsillach, ¶ 0050-0053];
Marsillach does not specifically state the participants single trajectory, rather the possible trajectory areas of motion of the participant.
Lin, which is also a vehicle control system that transforms vehicle locations into a Frenet system and further controls ego vehicles based on other vehicles teaches “predict a trajectory of the host vehicle and a trajectory of another vehicle” [Lin, ¶ 0070 and 0082].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Lin into the invention of Marsillach to not only include using machine learning tools while in the Frenet coordinate system to determing how to avoid other vehicles while in motion (Dynamic) as Marsillach discloses but to also account for actual other vehicle trajectories and not possible trajectories as taught by Lin with a reasonable expectation of success. One would be motivated to incorporate aspects of the cited prior art Lin into Marsillach to create a more robust system that can “identify whether there is a risk of a collision” [Lin, ¶ 0070] .Additionally, the claimed invention is merely a combination of old, well known elements such as vehicle control based on dynamic objects with AI tools in a Frenet coordinate system and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
Examiners Note - Intended Use
Claim 1 uses “intended use” claim language and it is the Office’s stance that this language is not clear, as currently presented, and does not carry much patentable weight. Using the Broadest Reasonable Interpretation (BRI), the terms “reduces sensitivity” that is recited in the above mentioned claims conveys the “intended use” of certain elements of each claim and for examining purposes, only the elements with patentable weight and not the uses need to be addressed.
For example, Claim 1 states “reduces sensitivity of the resulting Frenet representation to curvature of the roadway” and this intended use language would not carry patentable weight since it is the intended use of the invention. It appears that any use of the Frenet coordinate system would achieve this function thus what is actually being claimed is a benefit of using the Frenet system. Further it appears any system using the Frenet coordinate system would have the same benefit. The Office suggests rewriting these claims to remove the “intended use" phrases which would make the claims more clear and better mark the metes and bounds of the claimed subject matter.
With respect to Claim 2: Marsillach discloses “The method according to claim 1, wherein at least one track sequence is determined” [Marsillach, ¶ 0032-0035 with Figure 2];
“wherein the at least one track sequence includes the current track section and a possible continuation of the current track section” [Marsillach, ¶ 0032-0035 with Figure 2];
“and the at least one track sequence specifies the reference path for the Frenet transformation of the scene representation” [Marsillach, ¶ 0032-0035 with Figure 2].
With respect to Claim 3: Marsillach discloses “The method according to claim 2, wherein, based on a combination of the aggregated scene-specific information obtained from at least one sensor with map information, a determination is made of” [Marsillach, ¶ 0032-0035 with Figure 2];
the current position of the at least one participant” [Marsillach, ¶ 0032-0035 with Figure 2];
the current track of the at least one participant” [Marsillach, ¶ 0032-0035 with Figure 2];
(c) the at least one track sequence of the at least one participant” [Marsillach, ¶ 0032-0035 with Figure 2].
With respect to Claim 4: Marsillach discloses “The method according to claim 2, wherein at least two different track sequences are determined” [Marsillach, ¶ 0007, 0032-0035 with Figure 2];
“the scene representation is transformed into at least two different Frenet coordinate systems” [Marsillach, ¶ 0007, 0032-0035 with Figure 2];
“wherein one of the at least two different track sequences specifies the reference path for the respective Frenet transformation” [Marsillach, ¶ 0007, 0032-0035 with Figure 2];
“and the pretrained AI prediction model is used for the prediction for all resulting Frenet representations of the traffic scene” [Marsillach, ¶ 0050-0052].
With respect to Claim 5: While Marsillach discloses “The method according to claim 2, wherein the AI prediction model is an AI prediction model trained with Frenet-transformed training representations of different traffic scenes with at least one participant” [Marsillach, ¶ 0018, 0037-0038 and 0047];
“wherein, for each of the training representation, a future trajectory area of the participant was known as ground truth” [Marsillach, ¶ 0018, 0037-0038 and 0047];
“and wherein a track sequence that was as similar as possible to the ground truth was used in each case as the reference path for the Frenet transformation of the training representations” [Marsillach, ¶ 0018, 0037-0038 and 0047];
Marsillach does not specifically state the participants trajectory, rather the possible trajectory areas of motion of the participant.
Lin, which is also a vehicle control system that transforms vehicle locations into a Frenet system and further controls ego vehicles based on other vehicles teaches “predict a trajectory of the host vehicle and a trajectory of another vehicle” [Lin, ¶ 0070 and 0082].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Lin into the invention of Marsillach to not only include using machine learning tools while in the Frenet coordinate system to determing how to avoid other vehicles while in motion (Dynamic) as Marsillach discloses but to also account for actual other vehicle trajectories and not possible trajectories as taught by Lin with a reasonable expectation of success. One would be motivated to incorporate aspects of the cited prior art Lin into Marsillach to create a more robust system that can “identify whether there is a risk of a collision” [Lin, ¶ 0070] .Additionally, the claimed invention is merely a combination of old, well known elements such as vehicle control based on dynamic objects with AI tools in a Frenet coordinate system and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
With respect to Claim 6: Marsillach discloses “The method according to claim 1, wherein the prediction using the AI prediction model provides trajectories/areas in the Frenet coordinate system of the Frenet representation of the traffic scene” [Marsillach, ¶ 0007, 0032-0035 with Figure 2];
Marsillach does not specifically state the participants trajectory, rather the possible trajectory areas of motion of the participant.
Lin, which is also a vehicle control system that transforms vehicle locations into a Frenet system and further controls ego vehicles based on other vehicles teaches “predict a trajectory of the host vehicle and a trajectory of another vehicle” [Lin, ¶ 0070 and 0082].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Lin into the invention of Marsillach to not only include using machine learning tools while in the Frenet coordinate system to determing how to avoid other vehicles while in motion (Dynamic) as Marsillach discloses but to also account for actual other vehicle trajectories and not possible trajectories as taught by Lin with a reasonable expectation of success. One would be motivated to incorporate aspects of the cited prior art Lin into Marsillach to create a more robust system that can “identify whether there is a risk of a collision” [Lin, ¶ 0070] .Additionally, the claimed invention is merely a combination of old, well known elements such as vehicle control based on dynamic objects with AI tools in a Frenet coordinate system and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
With respect to Claim 8: While Marsillach discloses “A computer-implemented system configured to predict at least one trajectory area of at least one participant in a current traffic scene and for operating a vehicle based on the predicted at least one trajectory area, the system comprising” [Marsillach, ¶ 0032-0035 with Figure 2];
“a. at least one sensor of the vehicle configured to acquire scene-specific information over a past observation horizon” [Marsillach, ¶ 0032-0035 with Figure 2];
“b. a perception layer configured to aggregate scene-specific information obtained from the at least one sensor over the past observation horizon, wherein the aggregated scene-specific information includes position and movement parameters of the at least one participant relative to a roadway” [Marsillach, ¶ 0032-0035 with Figure 2];
“c. an evaluation module configured to generate a scene representation of the current traffic scene based on the aggregated scene-specific information” [Marsillach, ¶ 0032-0035 with Figure 2];
“d. a localization module configured to determine a current position of the at least one participant and a current track section on which the at least one participant is currently located” [Marsillach, ¶ 0032-0035 with Figure 2];
“e. a first transformation module configured to transform the scene representation into at least one Frenet coordinate system to provide at least one resulting Frenet representation of the current traffic scene, wherein the current track section specifies at least one section of a reference path for the Frenet transformation, and wherein the Frenet transformation reduces sensitivity of the resulting Frenet representation to curvature of the roadway” [Marsillach, ¶ 0041-0044 and 0050];
“f. a pretrained artificial-intelligence-based (Al} prediction model configured to predict at least one future trajectory area of the at least one participant based on the at least one resulting Frenet representation of the current traffic scene; and” [Marsillach, ¶ 0050-0053];
g. a vehicle control or driver assistance system configured to use the predicted at least one future trajectory area to adapt operation of at least one vehicle function” [Marsillach, ¶ 0050-0053];
Marsillach does not specifically state the participants single trajectory, rather the possible trajectory areas of motion of the participant.
Lin, which is also a vehicle control system that transforms vehicle locations into a Frenet system and further controls ego vehicles based on other vehicles teaches “predict a trajectory of the host vehicle and a trajectory of another vehicle” [Lin, ¶ 0070 and 0082].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Lin into the invention of Marsillach to not only include using machine learning tools while in the Frenet coordinate system to determing how to avoid other vehicles while in motion (Dynamic) as Marsillach discloses but to also account for actual other vehicle trajectories and not possible trajectories as taught by Lin with a reasonable expectation of success. One would be motivated to incorporate aspects of the cited prior art Lin into Marsillach to create a more robust system that can “identify whether there is a risk of a collision” [Lin, ¶ 0070] .Additionally, the claimed invention is merely a combination of old, well known elements such as vehicle control based on dynamic objects with AI tools in a Frenet coordinate system and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
Examiners Note - Intended Use
Claim 8 uses “intended use” claim language and it is the Office’s stance that this language is not clear, as currently presented, and does not carry much patentable weight. Using the Broadest Reasonable Interpretation (BRI), the terms “reduces sensitivity” that is recited in the above mentioned claims conveys the “intended use” of certain elements of each claim and for examining purposes, only the elements with patentable weight and not the uses need to be addressed.
For example, Claim 8 states “reduces sensitivity of the resulting Frenet representation to curvature of the roadway” and this intended use language would not carry patentable weight since it is the intended use of the invention. It appears that any use of the Frenet coordinate system would achieve this function thus what is actually being claimed is a benefit of using the Frenet system. Further it appears any system using the Frenet coordinate system would have the same benefit. The Office suggests rewriting these claims to remove the “intended use" phrases which would make the claims more clear and better mark the metes and bounds of the claimed subject matter.
With respect to Claim 9: Marsillach discloses “The system according to claim 8, wherein the localization module is configured to determine at least one track sequence” [Marsillach, ¶ 0032-0035 with Figure 2];
“wherein the track sequence includes the current track section and a possible continuation of the current track section” [Marsillach, ¶ 0032-0035 with Figure 2];
“so that the first transformation module can determine the reference path for the Frenet transformation of the scene representation based on the track sequence” [Marsillach, ¶ 0032-0035, 0041-0044 and 0050].
With respect to Claim 11: Marsillach discloses “A computer implemented method for training an Artificial Intelligence (AI) prediction model of a system configured to predict at least one trajectory area of at least one participant of a traffic scene, in which training representations of different traffic scenes with at least one participant are used” [Marsillach, ¶ 0032-0035, 0065 with Figure 2],
“and in which a future trajectory area of the at least one participant is known as ground truth for each training representation, the method comprising the following steps for each of the training representations” [Marsillach, ¶ 0032-0038, 0047 with Figure 2];
“a. generating a training scene representation based on aggregated scene-specific information associated with the training representation, wherein the aggregated scene-specific information includes position and movement parameters of the at least one participant relative to a roadway” [Marsillach, ¶ 0032-0038, 0047-0049 with Figure 2];
“b. determining a current position of the at least one participant, a current track section on which the participant is located, and at least one track sequence that includes the current track section and a possible continuation of the current track section” [Marsillach, ¶ 0032-0035 with Figure 2];
“c. selecting, from the a least one track sequence that is most similar to the known ground truth future trajectory area” [Marsillach, ¶ 0032-0038, 0047-0049 with Figure 2];
“d. transforming the training representation into a Frenet coordinate system to provide a resulting Frenet representation, wherein the selected track sequence specifies the reference path for the Frenet transformation and wherein the Frenet transformation reduces sensitivity of the resulting Frenet representation to curvature of the roadway” [Marsillach, ¶ 0041-0044 and 0050];
“e. using the resulting Frenet representation as an input to the AI prediction model to predict at least one future trajectory area; and” [Marsillach, ¶ 0041-0044 and 0050];
“g. comparing the at least one predicted trajectory area with the ground truth future trajectory area and modifying parameters of the AI prediction model as a function of a result of the comparison” [Marsillach, ¶ 0032-0038, 0047-0049 with Figure 2];
Marsillach does not specifically state the participants single trajectory, rather the possible trajectory areas of motion of the participant.
Lin, which is also a vehicle control system that transforms vehicle locations into a Frenet system and further controls ego vehicles based on other vehicles teaches “predict a trajectory of the host vehicle and a trajectory of another vehicle” [Lin, ¶ 0070 and 0082].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Lin into the invention of Marsillach to not only include using machine learning tools while in the Frenet coordinate system to determing how to avoid other vehicles while in motion (Dynamic) as Marsillach discloses but to also account for actual other vehicle trajectories and not possible trajectories as taught by Lin with a reasonable expectation of success. One would be motivated to incorporate aspects of the cited prior art Lin into Marsillach to create a more robust system that can “identify whether there is a risk of a collision” [Lin, ¶ 0070] .Additionally, the claimed invention is merely a combination of old, well known elements such as vehicle control based on dynamic objects with AI tools in a Frenet coordinate system and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
Examiners Note - Intended Use
Claim 11 uses “intended use” claim language and it is the Office’s stance that this language is not clear, as currently presented, and does not carry much patentable weight. Using the Broadest Reasonable Interpretation (BRI), the terms “reduces sensitivity” that is recited in the above mentioned claims conveys the “intended use” of certain elements of each claim and for examining purposes, only the elements with patentable weight and not the uses need to be addressed.
For example, Claim 11 states “reduces sensitivity of the resulting Frenet representation to curvature of the roadway” and this intended use language would not carry patentable weight since it is the intended use of the invention. It appears that any use of the Frenet coordinate system would achieve this function thus what is actually being claimed is a benefit of using the Frenet system. Further it appears any system using the Frenet coordinate system would have the same benefit. The Office suggests rewriting these claims to remove the “intended use" phrases which would make the claims more clear and better mark the metes and bounds of the claimed subject matter.
With respect to Claim 12: While Marsillach discloses “The method according to claim 1, wherein the reference path for the Frenet transformation is defined” [Marsillach, ¶ 0032-0035, 0041-0044 and 0050 with Figure 2]
Marsillach does not specifically state that the Frenet coordinate system is based on the centerline or lane boundary, rather lateral and longitudinal values.
Lin, which is also a vehicle control system that transforms vehicle locations into a Frenet system and further controls ego vehicles based on other vehicles teaches “wherein the reference path for the Frenet transformation is defined by a lane centerline or a lateral boundary of the current track section” [Lin, ¶ 0068].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Lin into the invention of Marsillach to not only include using machine learning tools while in the Frenet coordinate system to determing how to avoid other vehicles while in motion (Dynamic) as Marsillach discloses but to also use the Frenet system based on centerline of the lane as taught by Lin with a reasonable expectation of success. One would be motivated to incorporate aspects of the cited prior art Lin into Marsillach to create a more robust system that can “identify whether there is a risk of a collision” [Lin, ¶ 0070] .Additionally, the claimed invention is merely a combination of old, well known elements such as vehicle control based on dynamic objects with AI tools in a Frenet coordinate system and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
With respect to Claim 13: Marsillach discloses “The method according to claim 1, further comprising determining at least one track sequence that includes the current track section and a possible continuation of the current track section, wherein the reference path for the Frenet transformation is specified by the at least one track sequence” [Marsillach, ¶ 0032-0035, 0041-0044 and 0050 with Figure 2].
With respect to Claim 14: Marsillach discloses “The method according to claim 13, wherein the scene representation 1s transformed into a plurality of Frenet coordinate systems corresponding to different track sequences, and wherein the pretrained artificial-intelligence-based prediction model is used to predict a respective future trajectory for each resulting Frenet representation” [Marsillach, ¶ 0032-0035, 0041-0044 and 0050 with Figure 2].
With respect to Claim 15: Marsillach discloses “The method according to claim 1, wherein the aggregated scene-specific information is based on a combination of acquired sensor data and map information associated with the roadway” [Marsillach, ¶ 0009, 0032-0035 with Figure 2].
With respect to Claim 16: While Marsillach discloses “The method according to claim 1, wherein the pretrained artificial-intelligence based prediction model provides the predicted at least one future trajectory area in the Frenet coordinate system of the resulting Frenet representation” [Marsillach, ¶ 0009, 0032-0035 with Figure 2];
Marsillach does not specifically state the participants single trajectory, rather the possible trajectory areas of motion of the participant.
Lin, which is also a vehicle control system that transforms vehicle locations into a Frenet system and further controls ego vehicles based on other vehicles teaches “predict a trajectory of the host vehicle and a trajectory of another vehicle” [Lin, ¶ 0070 and 0082].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Lin into the invention of Marsillach to not only include using machine learning tools while in the Frenet coordinate system to determing how to avoid other vehicles while in motion (Dynamic) as Marsillach discloses but to also account for actual other vehicle trajectories and not possible trajectories as taught by Lin with a reasonable expectation of success. One would be motivated to incorporate aspects of the cited prior art Lin into Marsillach to create a more robust system that can “identify whether there is a risk of a collision” [Lin, ¶ 0070] .Additionally, the claimed invention is merely a combination of old, well known elements such as vehicle control based on dynamic objects with AI tools in a Frenet coordinate system and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
With respect to Claim 17: Marsillach discloses “The method according to claim 1, wherein adapting operation of the at least one vehicle function comprises using the predicted at least one future trajectory as an input to a vehicle planning or control function for determining or implementing a trajectory of the vehicle” [Marsillach, ¶ 0009, 0032-0035 with Figure 2].
Claims 7 and 10 are rejected under 35 USC 103 as being unpatentable over Marsillach et al. (United States Patent Publication 2024/0132103) in view of Lin et al. (United States Patent Publication 2023/0054263) in further view of Ng et al. (United States Patent Publication 2024/0059285).
With respect to Claim 7: While Marsillach discloses “The method according to claim 1, wherein the predicted at least one future trajectory area is transformed” [Marsillach, ¶ 0032-0035 with Figure 2];
Marsillach does not specifically state transforming the trajectory back into cartesian coordinates.
Ng, which is also a vehicle control system that uses the Frenet coordinate system, objects and trajectories with AI models for trajectory planning and vehicle control teaches “wherein the predicted at least one future trajectory is transformed into a comparison coordinate system, the comparison coordinate system including a local Cartesian coordinate system of an observing participant of the traffic scene” [Ng, ¶ 0003, 0033-0035 with Figures 1-2].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Ng into the invention of Marsillach to not only include using machine learning tools while in the Frenet coordinate system to determing how to avoid other vehicles while in motion (Dynamic) as Marsillach discloses but to also transform the areas/trajectories back into the cartesian coordinate system as taught by Ng with a reasonable expectation of success. One would be motivated to incorporate aspects of the cited prior art Ng into Marsillach to create a more robust system that can have the trajectories in a variety or coordinates systems for ease of use. Additionally, the claimed invention is merely a combination of old, well known elements such as vehicle control based on dynamic objects with AI tools in a Frenet coordinate system and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
With respect to Claim 10: While Marsillach discloses “The system according to claim 8, further comprising: a second transformation module configured to transform the at least one predicted trajectory” [Marsillach, ¶ 0032-0035 with Figure 2];
Marsillach does not specifically state transforming the trajectory back into cartesian coordinates.
Ng, which is also a vehicle control system that uses the Frenet coordinate system, objects and trajectories with AI models for trajectory planning and vehicle control teaches “wherein the predicted at least one future trajectory is transformed into a comparison coordinate system, the comparison coordinate system including a local Cartesian coordinate system of an observing participant of the traffic scene” [Ng, ¶ 0003, 0033-0035 with Figures 1-2].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Ng into the invention of Marsillach to not only include using machine learning tools while in the Frenet coordinate system to determing how to avoid other vehicles while in motion (Dynamic) as Marsillach discloses but to also transform the areas/trajectories back into the cartesian coordinate system as taught by Ng with a reasonable expectation of success. One would be motivated to incorporate aspects of the cited prior art Ng into Marsillach to create a more robust system that can have the trajectories in a variety or coordinates systems for ease of use. Additionally, the claimed invention is merely a combination of old, well known elements such as vehicle control based on dynamic objects with AI tools in a Frenet coordinate system and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
Prior Art (Not relied upon)
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure can be found in the attached form 892.
Conclusion
Applicant’s amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).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 extension fee 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 date of this final action.
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/JESS WHITTINGTON/Primary Examiner, Art Unit 3666c