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 .
Response to Amendment
The following is a final office action in response to the communication filed on 04/06/2026. Claims 1-7 are currently pending and have been examined.
Response to Arguments
Applicant’s arguments and remarks filed on 04/06/2026 have been fully considered.
Applicant’s amendments overcome the objections to the specification.
Applicant’s arguments provided for the U.S.C. §102 and §103 rejections of claims 1-7 have been considered but are not persuasive.
(A) Applicant argues, “Claims 1-4 and 6-7 were rejected under 35 U.S.C. § 102(a)(2) as allegedly being anticipated by Eix et al. (WO-2021130074-Al; hereinafter Eix). Office Action at p. 3. Applicant traverses the rejections.
“Applicant disagrees with the rejection. Applicant submits that Eix fails to disclose each and every element of claim 1. In rejecting claim 1, the Office Action cites portions of Eix. In particular, Eix describes that, "in process step S7, a classification takes place. For this purpose, errors between radar-based and model-based buckling angles are used. Through comparison, the vehicle combination model that best describes the vehicle combination is selected from the multitude of vehicle combination models. This is done taking into account that different trailer types move differently due to their different kinematics, which is reflected in the various vehicle combination models. Accordingly, the error of a vehicle combination model that describes the kinematics of the actual trailer is generally smaller than for a vehicle combination model that does not take this kinematics into account. Furthermore, the information about the cycling and thus axle positions and, in particular, the detected number of axles of at least one other vehicle are evaluated, so that the trailer types can be clearly distinguished in many cases," Eix [0047].
“Thus, the cited portions of Eix describe a classification step that selects a vehicle combination model (e.g., corresponding to a trailer type) from among multiple vehicle combination models. However, Eix does not disclose the specific combination of claim 1. More specifically, Eix fails to disclose ‘comparing measured changes in the location data with the predicted changes and deciding, using a classification algorithm and using results of the comparison, whether the location data represents a trailer or not.’
“Eix's classification step does not decide whether the location data represents a trailer or not. Instead, Eix uses the comparison to select a vehicle combination model that best describes the vehicle combination, taking into account that different trailer types move differently due to different kinematics. Relatedly, Eix explains that "the information regarding the position and/or number of wheels or axles of the at least one other vehicle... [is] used to detect the type of trailer,"Eix p. 3. Eix also describes example vehicle combinations as including a trailer (e.g., "a truck with a trailer,""a tractor unit with a semi-trailer,""a combination of a solo bus and bus trailer,""a caravan," etc.), Eix p. 6, and further notes that "the invention is not limited to a vehicle combination with a specific number of vehicles, but is particularly applicable to any number of trailers,"Eix pp. 6-7.
“Accordingly, Eix is directed to selecting among trailer-related vehicle combination models (e.g., determining a trailer type), rather than deciding whether a detected object is a trailer in the first place. Eix therefore fails to disclose "comparing measured changes in the location data with the predicted changes and deciding, using a classification algorithm and using results of the comparison, whether the location data represents a trailer or not," as recited in claim 1.
“For at least the reasons above, Eix fails to disclose each and every element of claim 1. Thus, claim 1 is patentable,” (from remarks pages 5-7).
As to point (A), Examiner respectfully disagrees. Applicant asserts that the Eix reference does not teach the limitation of claim 1 of “deciding…whether the location data represents a trailer or not”. Examiner’s interpretation of this limitation relies on Eix’s teaching of comparing the measured changes in the location data with the predicted changes for multiple trailer types (see translation [0047]; “Through comparison, the vehicle combination model that best describes the vehicle combination is selected from the multitude of vehicle combination models. This is done taking into account that different trailer types move differently due to their different kinematics, which is reflected in the various vehicle combination models…”). For each of the “multitude of vehicle combination models”, the method considers a trailer and decides whether the location data represents this trailer or not. For all but one of the trailers considered, Eix teaches deciding that the location data does not represent that trailer. For one of the trailers considered, Eix teaches deciding that the location data does represent that trailer. Therefore, under broadest reasonable interpretation, Eix teaches the limitation of “comparing measured changes in the location data with the predicted changes and deciding, using a classification algorithm and using results of the comparison, whether the location data represents a trailer or not.”
(B) Applicant argues, “Independent claim 7 recites similar language as claim 1 and is also patentable for the same reasons set forth above. Claims 2-4 and 6 are likewise patentable, at least by virtue of depending from a patentable claim.
“Rejection of Claim 5 Under 35 U.S.C. § 103
“Claim 5 was rejected under 35 U.S.C. § 103 as allegedly being unpatentable over Eix in view of Armbruster et al. (WO-2022224858-A 1; hereinafter Armbruster). Office Action at p. 9. Applicant disagrees with the rejection.
“Claim 5 depends from claim 1. As explained above, claim 1 is patentable over Eix. Armbruster does not, and the Office has not shown that it would, cure the deficiencies of Eix.
“Claim 5 is therefore patentable over Eix and Armbruster for at least the same reasons as set forth above. Reconsideration and withdrawal of the rejection is requested,” (from remarks pages 6-7).
As to point (B), see point (A).
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-4 and 6-7 are rejected under 35 U.S.C. 102(a) as being anticipated by Eix et al. (WO-2021130074-A1; hereinafter Eix).
Regarding claim 1, Eix discloses:
A method (see translation at least [0042]; “Figure 3 shows a schematic flowchart of a procedure for determining at least one articulation angle of a vehicle combination.”) for detecting a trailer (see translation at least [0048]; “Axle positions and, in particular, the detected number of axles of at least one other vehicle are evaluated, so that the trailer types can be clearly distinguished in many cases.”) being towed by a towing vehicle (see translation at least [0008]; “The angle of articulation between the towing vehicle and the trailer, or more generally between two vehicles in the vehicle combination, is important.”) using a radar system of the towing vehicle (see translation at least [0012]; “Radar data is acquired by at least one radar sensor mounted on one vehicle of the vehicle combination.”), comprising the following steps:
estimating parameters of a trailer model using location data of the radar system (see translation at least [0044]; “In a further procedural step, for each vehicle combination model, the articulation angle and/or axle positions of at least one further vehicle of the vehicle combination, preferably a trailer, are estimated, S3n. In this step of the process, the radar data is used to determine the location and speed of reflective objects in the environment of the vehicle combination. Based on the estimated articulation angle and/or the estimated axle position, those radar data are selected which can be assigned to at least one other vehicle of the vehicle combination. This allows the radar data to be assigned to the trailer.”);
predicting changes in the location data using the trailer model and using movement data of the towing vehicle (see translation at least [0020]; “According to a further embodiment of the method for determining at least one articulation angle of the vehicle combination, the at least one articulation angle is estimated using driving information from the vehicle on which the radar sensor is located. The driving information can include at least one instantaneous speed, instantaneous yaw rate, instantaneous steering angle and a general vehicle parameter of the vehicle on which the radar sensor is located.”); and
comparing measured changes in the location data with the predicted changes and deciding, using a classification algorithm and using results of the comparison, whether the location data represents a trailer or not (see translation at least [0047]; “In process step S7, a classification takes place. For this purpose, errors between radar-based and model-based buckling angles are used. Through comparison, the vehicle combination model that best describes the vehicle combination is selected from the multitude of vehicle combination models. This is done taking into account that different trailer types move differently due to their different kinematics, which is reflected in the various vehicle combination models. Accordingly, the error of a vehicle combination model that describes the kinematics of the actual trailer is generally smaller than for a vehicle combination model that does not take this kinematics into account. Furthermore, the information about the cycling and thus axle positions and, in particular, the detected number of axles of at least one other vehicle are evaluated, so that the trailer types can be clearly distinguished in many cases.”).
Regarding claim 2, Eix discloses the method according to claim 1. Eix further teaches:
wherein in which the parameters of the trailer model include at least one of the following variables: a distance between a hitch position and an axle of the trailer, a number of axles of the trailer (see translation at least [0017]; “According to a further embodiment of the method for determining at least one articulation angle of the vehicle combination, the calculation of the at least one position information includes determining a micro-Doppler information based on the determined radar data, wherein a position and/or number of wheels of the at least one further vehicle is determined based on the micro-Doppler information. Due to the rotation of the rims, areas of the rims move towards the radar sensor and other areas move away from the radar sensor. This typical velocity distribution can be detected using the micro-Doppler effect, allowing the position of the wheels to be determined precisely. Information regarding the position and/or number of wheels or axles of at least one other vehicle can be used to identify the type of trailer and further increases the accuracy of the determined articulation angle.”), a wheelbase of a multi-axle trailer, a track width of the trailer, a width of the trailer, a length of a trailer, and a height of the trailer.
Regarding claim 3, Eix discloses the method according to claim 1. Eix further teaches:
wherein in the parameters of the trailer model are continuously updated using current location data (see translation at least [0048]; “During ferry operations, steps S2 to S6n are repeated”. See also description of step S3n in [0044]; “In a further procedural step, for each vehicle combination model, the articulation angle and/or axle positions of at least one further vehicle of the vehicle combination, preferably a trailer, are estimated, S3n. In this step of the process, the radar data is used to determine the location and speed of reflective objects in the environment of the vehicle combination. Based on the estimated articulation angle and/or the estimated axle position, those radar data are selected which can be assigned to at least one other vehicle of the vehicle combination. This allows the radar data to be assigned to the trailer.”).
Regarding claim 4, Eix discloses the method according to claim 1. Eix further teaches:
wherein wheels of an object potentially to be classified as a trailer are recognized using a micro-Doppler effect (see translation at least [0017]; “According to a further embodiment of the method for determining at least one articulation angle of the vehicle combination, the calculation of the at least one position information includes determining a micro-Doppler information based on the determined radar data, wherein a position and/or number of wheels of the at least one further vehicle is determined based on the micro-Doppler information. Due to the rotation of the rims, areas of the rims move towards the radar sensor and other areas move away from the radar sensor. This typical velocity distribution can be detected using the micro-Doppler effect, allowing the position of the wheels to be determined precisely. Information regarding the position and/or number of wheels or axles of at least one other vehicle can be used to identify the type of trailer and further increases the accuracy of the determined articulation angle.”).
Regarding claim 6, Eix discloses the method according to claim 1. Eix further teaches:
wherein the trailer model also includes a model of a towed vehicle (see translation at least [0007]; “Driver assistance systems for vehicle combinations require information regarding the position of the attached vehicles relative to the towing vehicle.” See also [0019]; “According to a further embodiment of the method for determining at least one articulation angle of the vehicle combination, the determined radar data are assigned to the at least one other vehicle of the vehicle combination using the at least one articulation angle estimated on the basis of the vehicle combination model.”).
Regarding claim 7, Eix discloses:
A towing vehicle (see translation at least [0037]; “The vehicle combination can be a truck and trailer combination, i.e. A truck with a trailer, a semi-trailer truck, i.e.
Tractor unit with semi-trailer, a bus combination, i.e. a combination of a solo bus and bus trailer, a road train, i.e. a combination of a towing vehicle and attached passenger car, a caravan combination, i.e. Towing vehicle with caravan, a motorcycle combination, i.e. Trade in a motorcycle with a trailer, a tractor with a trailer, or the like. The invention is not limited to a The vehicle combination is not limited to a specific number of vehicles, but is particularly applicable to any number of trailers.”), comprising:
at least one radar sensor (see translation at least [0013]; “The interface receives radar data, which is determined by at least one radar sensor located on one vehicle of the vehicle combination.”); and an electronic evaluation device (see translation at least [0036]; “The device 1 can be integrated into the radar sensor or be part of a driver assistance system. In particular, the device 1 can also be located in the vehicle in which the at least one radar sensor 4 is located, i.e. preferably in the towing vehicle.”) configured to detect trailer being towed by the towing vehicle (see translation at least [0013]; “According to a second aspect, the invention relates to a device for determining at least one articulation angle of a vehicle combination, comprising an interface and a computing device.”) using the radar sensor of the towing vehicle (see translation at least [0013]; “Based on the radar data obtained, the computing device calculates at least one position piece of information which relates to the position of at least one other vehicle in the vehicle combination.”), the electronic evaluation device being configured to:
estimate parameters of a trailer model using location data of the radar system (see translation at least [0044]; “In a further procedural step, for each vehicle combination model, the articulation angle and/or axle positions of at least one further vehicle of the vehicle combination, preferably a trailer, are estimated, S3n. In this step of the process, the radar data is used to determine the location and speed of reflective objects in the environment of the vehicle combination. Based on the estimated articulation angle and/or the estimated axle position, those radar data are selected which can be assigned to at least one other vehicle of the vehicle combination. This allows the radar data to be assigned to the trailer.”),
predict changes in the location data using the trailer model and using movement data of the towing vehicle (see translation at least [0020]; “According to a further embodiment of the method for determining at least one articulation angle of the vehicle combination, the at least one articulation angle is estimated using driving information from the vehicle on which the radar sensor is located. The driving information can include at least one instantaneous speed, instantaneous yaw rate, instantaneous steering angle and a general vehicle parameter of the vehicle on which the radar sensor is located.”), and
compare measured changes in the location data with the predicted changes and deciding, using a classification algorithm and using results of the comparison, whether the location data represents a trailer or not (see translation at least [0047]; “In process step S7, a classification takes place. For this purpose, errors between radar-based and model-based buckling angles are used. Through comparison, the vehicle combination model that best describes the vehicle combination is selected from the multitude of vehicle combination models. This is done taking into account that different trailer types move differently due to their different kinematics, which is reflected in the various vehicle combination models. Accordingly, the error of a vehicle combination model that describes the kinematics of the actual trailer is generally smaller than for a vehicle combination model that does not take this kinematics into account. Furthermore, the information about the cycling and thus axle positions and, in particular, the detected number of axles of at least one other vehicle are evaluated, so that the trailer types can be clearly distinguished in many cases.”).
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.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Eix in view of Armbruster et al. (WO-2022224858-A1; hereinafter Armbruster).
Regarding claim 5, Eix discloses the method according to claim 1. However, Eix does not teach:
wherein machine learning is used to optimize the classification algorithm.
Eix discloses determining an articulation angle of a vehicle combination (such as a trailer and towing vehicle) using radar. Armbruster is directed to determining a towed trailer size using radar of the towing vehicle. Armbruster teaches:
wherein machine learning is used to optimize the classification algorithm (see at least [0039]; “At 440, the control module 220 analyzes radar returns from a radar of the towing vehicle to identify radar features within an area behind the towing vehicle 100. In one arrangement, the control module 220 identifies cells in the grid that correspond with patterns indicative of characteristics of the trailer. The radar features are peaks and valleys in signal strengths of radar returns that correspond with edges of the trailer and parts of the trailer. Thus, the control module 220 identifies, in one approach, neighboring cells with similar or dissimilar radar returns. Where similarities exist, the control module 220 may indicate correspondence. In one approach, similarities are determined according to an extent of correspondence in signal values (e.g., a defined threshold of 70%). Even still, areas of similarities may not be sufficient in some cases to identify radar features associated with the trailer. Thus, in further approaches, the control module 220 may implement further analysis, such as linear regression, machine learning, or another approach to correlate the grid cells.”).
Both Eix and Armbruster determine properties of a trailer, such as trailer dimensions, using radar measurements from a towing vehicle. Eix includes a step for assigning which radar data belongs to a trailer (see translation at least [0044]). Armbruster teaches that the step of assigning which radar data belongs to a trailer may be improved through use of machine learning analysis (see at least [0039]). It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to implement the machine learning of Armbruster in the method of Eix in order to improve the identification of radar features associated with the trailer, as taught by Armbruster.
Conclusion
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ashley B. Raynal whose telephone number is (703)756-4546. The examiner can normally be reached Monday - Friday, 8 AM - 4 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vladimir Magloire can be reached at (571) 270-5144. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ASHLEY BROWN RAYNAL/Examiner, Art Unit 3648
/VLADIMIR MAGLOIRE/Supervisory Patent Examiner, Art Unit 3648
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