DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This is a Non-final Office Action on the merits. Claims 11-20 are currently pending and are addressed below.
Priority
Acknowledgement is made of applicant’s claim of priority for foreign application DE10 2022 114 589.1, filed 06/09/2022.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 12/02/2024 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered by the examiner.
Claim Objections
Claim 14 objected to because of the following informalities:
Claim 14 recites “said data”. Examiner respectfully recommends modifying the term “said data” to clearly indicate which data is being referenced.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 11-20 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.
Claims 11 and 19 recite “various data sources”. It is unclear whether this is referring to the same data sources recited in lines 6 and 10-11, respectively (“several different data sources”).
Claim 12 recites “wherein the data and/or data sources”. It is unclear whether “data” is referring to “data items” recited in parent claim 11 and whether “data sources” is referring to the same data sources as “several different data sources” and “various data sources” recited in parent claim 11.
Claim 12 recites the limitation "the respective instant" in lines 5-6. There is insufficient antecedent basis for this limitation in the claim.
Claim 13 recites “for at least one data source and/or data type”. It is unclear whether “at least one data source” is referring to one of the “several different data sources” or “various data sources” recited in parent claim 11. It is also unclear whether “data type” is referring to the “data items” in parent claim 11.
Claim 15 recites “a distance, as far as which, starting from a current position of the motor vehicle, at least one of the data sources and/or at least some of the data is/are to be used for predicting the driving path, is ascertained as a function of the environmental scenario ascertained in a given case”. It is unclear whether the distance, the data source/data, or both, is/are ascertained as a function of the environmental scenario ascertained in a given case.
Claim 16 recites “data”. It is unclear whether this is referring to the “data items” recited in parent claim 11.
Claim 16 recites “the prediction of the driving path” in line 4. There is insufficient antecedent basis for this limitation in the claim since parent claim 11 does not positively recite predicting the driving path.
Claim 17 recites “the data” and “said data”. It is unclear whether this is referring to the “data items” recited in parent claim 11.
Claim 18 recites “the predicted driving path” in line 3. There is insufficient antecedent basis for this limitation in the claim since parent claim 11 does not positively recite predicting the driving path.
Claim 20 recites “an assistance system”. It is unclear whether this is referring to the same assistance system recited in the preamble of parent claim 19.
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 11-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Independent Claim 11:
Step 1: Claim 11 is directed to a method for predicting a driving path of a motor vehicle (i.e., a process). Therefore, claim 11 is within at least one of the four statutory categories.
Step 2A Prong 1: Regarding Prong 1 of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity and/or c) mental processes.
Independent claim 11 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. Claim 11 recites:
A method for predicting a driving path of a motor vehicle, wherein during the operation of the motor vehicle, the method comprises:
ascertaining a respective environmental scenario currently situated ahead of the motor vehicle in a direction of travel;
ascertaining, for the respective ascertained environmental scenario, degrees of trustworthiness of several different data sources and/or data items originating from said data sources and on the basis of which the driving path is predictable; and
amalgamating together several of the data items originating from the various data sources in a weighted manner in accordance with the ascertained degrees of trustworthiness; and
using the amalgamated data to predict the driving path.
The examiner submits that the foregoing bolded limitations constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitations in the human mind. For example, “ascertaining a respective environmental scenario” in the context of this claim encompasses mentally ascertaining the environment. The limitation “ascertaining, for the respective ascertained environmental scenario, degrees of trustworthiness of several different data sources and/or data items originating from said data sources and on the basis of which the driving path is predictable” in the context of this claim encompasses mentally ascertaining degrees of trustworthiness for data sources or data items. The limitation “amalgamating together several of the data items originating from the various data sources in a weighted manner in accordance with the ascertained degrees of trustworthiness” encompasses mentally amalgamating or combining data items and mentally assigning a score based on degrees of trustworthiness. The limitation “using the amalgamated data to predict the driving path” encompasses mentally predicting a driving path using the amalgamated data.
Step 2A Prong 2: Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
A method for predicting a driving path of a motor vehicle, wherein during the operation of the motor vehicle, the method comprises:
ascertaining a respective environmental scenario currently situated ahead of the motor vehicle in a direction of travel;
ascertaining, for the respective ascertained environmental scenario, degrees of trustworthiness of several different data sources and/or data items originating from said data sources and on the basis of which the driving path is predictable; and
amalgamating together several of the data items originating from the various data sources in a weighted manner in accordance with the ascertained degrees of trustworthiness; and
using the amalgamated data to predict the driving path.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
The additional limitations of the “wherein during the operation of the motor vehicle” and “currently situated ahead of the motor vehicle in a direction of travel” merely integrate the abstract ideas into a generic or general purpose vehicle control environment.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B: Regarding Step 2B of the 2019 PEG, representative independent claim 11 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to the integration of the abstract idea into a practical application, the additional limitations of the “wherein during the operation of the motor vehicle” and “currently situated ahead of the motor vehicle in a direction of travel” are recited at a high level of generality such that they merely integrate the abstract ideas into a generic or general purpose vehicle control environment.
Therefore, claim 11 is ineligible under 35 U.S.C §101.
Regarding Independent Claim 19:
Step 1: Claim 19 is directed to a method for predicting a driving path of a motor vehicle (i.e., a process). Therefore, claim 19 is within at least one of the four statutory categories.
Step 2A Prong 1: Regarding Prong 1 of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity and/or c) mental processes.
Independent claim 19 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. Claim 19 recites:
An assistance system for a motor vehicle, comprising:
an interface for capturing various data usable for predicting a driving path;
a processor and a computer-readable data memory coupled with the interface, wherein the assistance system is configured to:
ascertain a respective environmental scenario currently situated ahead of the motor vehicle in a direction of travel;
ascertain, for the respective ascertained environmental scenario, degrees of trustworthiness of several different data sources and/or data items originating from said data sources and on the basis of which the driving path is predictable; and
amalgamating together several of the data items originating from the various data sources in a weighted manner in accordance with the ascertained degrees of trustworthiness; and
use the amalgamated data to predict the driving path.
The examiner submits that the foregoing bolded limitations constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitations in the human mind. For example, “ascertaining a respective environmental scenario” in the context of this claim encompasses mentally ascertaining the environment. The limitation “ascertaining, for the respective ascertained environmental scenario, degrees of trustworthiness of several different data sources and/or data items originating from said data sources and on the basis of which the driving path is predictable” in the context of this claim encompasses mentally ascertaining degrees of trustworthiness for data sources or data items. The limitation “amalgamating together several of the data items originating from the various data sources in a weighted manner in accordance with the ascertained degrees of trustworthiness” encompasses mentally amalgamating or combining data items and mentally assigning a score based on degrees of trustworthiness. The limitation “using the amalgamated data to predict the driving path” encompasses mentally predicting a driving path using the amalgamated data.
Step 2A Prong 2: Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
An assistance system for a motor vehicle, comprising:
an interface for capturing various data usable for predicting a driving path;
a processor and a computer-readable data memory coupled with the interface, wherein the assistance system is configured to:
ascertain a respective environmental scenario currently situated ahead of the motor vehicle in a direction of travel;
ascertain, for the respective ascertained environmental scenario, degrees of trustworthiness of several different data sources and/or data items originating from said data sources and on the basis of which the driving path is predictable; and
amalgamating together several of the data items originating from the various data sources in a weighted manner in accordance with the ascertained degrees of trustworthiness; and
use the amalgamated data to predict the driving path.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
The additional limitation “an interface for capturing various data usable for predicting a driving path” is recited at a high level of generality and considered insignificant extra-solution activity (i.e., data gathering). The additional limitations of the “a processor and a computer-readable data memory coupled with the interface, wherein the assistance system is configured to” and “currently situated ahead of the motor vehicle in a direction of travel” merely integrate the abstract ideas into a generic or general purpose vehicle control environment.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B: Regarding Step 2B of the 2019 PEG, representative independent claim 19 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to the integration of the abstract idea into a practical application, the additional limitation “an interface for capturing various data usable for predicting a driving path” is recited at a high level of generality and considered insignificant extra-solution activity (i.e., data gathering). The additional limitations of “an interface”, “a processor and a computer-readable data memory coupled with the interface, wherein the assistance system is configured to” and “currently situated ahead of the motor vehicle in a direction of travel” are recited at a high level of generality such that they merely integrate the abstract ideas into a generic or general purpose vehicle control environment.
Therefore, claim 19 is ineligible under 35 U.S.C §101.
Dependent Claims
Dependent claims 12-18 and 20 do not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception. Dependent claim 12 further describes the data and/or data sources, dependent claim 13 is further directed to the abstract idea of inferring degrees of trustworthiness, dependent claim 14 is further directed to recording environmental data (i.e., data gathering), dependent claim 15 is further directed to the abstract idea of ascertaining a distance, dependent claim 16 is further directed to the abstract idea of incorporating into the amalgamation and prediction of the driving path, dependent claim 17 is further directed to the abstract idea of ascertaining uncertainty and weighing data, dependent claim 18 is further directed to the abstract idea of selecting objects in the respective environment of the motor vehicle, and dependent claim 20 is further directed to data gathering and additional elements that merely integrate the abstract idea into a generic vehicle control environment.
Therefore, claim(s) 11-20 is/are ineligible under 35 U.S.C. §101.
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.
Claim(s) 11-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Fradin of DE 102017106349 A1, filed 03/24/2017 (published 09/27/2018), hereinafter “Fradin”.
Regarding claim 11, Fradin discloses:
A method for predicting a driving path of a motor vehicle, wherein during the operation of the motor vehicle, the method comprises: (See at least [0001]: “The invention relates to a driver assistance system for a vehicle, which is hereinafter also referred to as the own vehicle, for predicting a lane area ahead of the own vehicle in order to determine whether an object, such as a vehicle driving ahead of the own vehicle, is located in the predicted lane area…”)
ascertaining a respective environmental scenario currently situated ahead of the motor vehicle in a direction of travel; (See at least Fig. 1 & [0046]: “…The environmental sensors 3 are designed to detect at least part of the vehicle's surroundings, and in particular lane markings 4 of a currently driven roadway 5. A navigation device, such as a GPS sensor and an associated map, can also be provided as an additional environmental sensor 3, for example to determine whether the vehicle 1 is currently driving on a straight road or a curved road…”)
ascertaining, for the respective ascertained environmental scenario, degrees of trustworthiness of several different data sources and/or data items originating from said data sources and on the basis of which the driving path is predictable; and (See at least [0065]: “Figure 1 shows a schematic representation of a roadway 5 with a vehicle 1 and a vehicle 14 driving in front of the vehicle 1. Furthermore, the first lane area 12 calculated according to the first determination method, as well as the lane area 13 calculated according to the second determination method, are shown again, in particular for a specific time. In this example, reliability values W1, W2 can now be calculated for each lane area 12 , 13…” & [0071]: “…Furthermore, for a respective defined distance range A1, A2, A3, A4, a reliability value W1, W2 is calculated in step S41 for the respective calculated lane areas 12, 13…”. See also [0010] regarding the first lane area being determined based on self-motion parameters of the vehicle and the second lane area being determined based on detected lane markings and [0018] regarding using environmental data for determining a reliability value.)
amalgamating together several of the data items originating from the various data sources in a weighted manner in accordance with the ascertained degrees of trustworthiness; and (See at least [0071]: “…Furthermore, in step S42, for each of the distance ranges A1, A2, A3, A4, the first lane area 12 and the second lane area 13 are weighted accordingly according to their respective reliability values W1, W2 and averaged” & [0015]: “…Such an averaging does not necessarily have to take into account the results, i.e. the first lane area and the second lane area, of these two determination methods in the same way, but can also be weighted, for example, so that it is possible to specifically exploit the strengths of the respective determination methods and to compensate for their weaknesses…”)
using the amalgamated data to predict the driving path. (See at least [0071]: “…The result of this averaging is provided as a predicted lane area in step S43 and checked in step S44 to see whether the measured position of the vehicle 14 ahead lies within the predicted lane area or not. If this is the case, the vehicle 14 in front is selected as the destination in step S 45; otherwise, it is not selected in step S 46.”)
Regarding claim 12, Fradin discloses all the limitations of claim 11 as discussed above.
Fradin additionally discloses:
wherein the data and/or data sources comprise predetermined map data, a road model for estimating a road contour situated ahead, an estimated road contour situated ahead, a detection of a roadway-edge, cluster data specifying earlier vehicle movements, live trajectories of other road-users moving within the respective environmental scenario at the respective instant, a maneuver hypothesis of an assistance system of the motor vehicle, steering data pertaining to the motor vehicle, a yaw-rate of the motor vehicle and/or a driving-path prediction of a device for machine learning. (See at least [0014]: “To determine the first lane area based on at least two self-motion parameters, several self-motion parameters are preferably used, such as…a yaw angle or yaw rate…”)
Regarding claim 13, Fradin discloses all the limitations of claim 11 as discussed above.
Fradin additionally discloses:
wherein the degrees of trustworthiness are inferred at least partially from a predetermined map in which a location-specific degree of trustworthiness has been specified for at least one data source and/or data type. (See at least [0025]: “…Navigation data, for example from a navigation device which may include a GPS receiver or other positioning device, as well as map data which may be stored in a memory, can also be used to assess reliability. As mentioned at the beginning, the reliability of lane area calculation based on self-motion is relatively low on curved roads, while it is very high on straight roads, for example. Whether the currently driven roadway or the route ahead is straight or curved can thus be determined based on the corresponding navigation data. It is not absolutely necessary to derive the curvature of the preceding section of the route directly from the navigation data; for example, it can also be inferred from the type of road currently being traveled. For example, highways typically run in a straighter line than country roads or even streets within towns and cities…”)
Regarding claim 14, Fradin discloses all the limitations of claim 11 as discussed above.
Fradin additionally discloses:
wherein environmental data that characterize the respective environmental scenario are recorded via environmental sensorics of the motor vehicle during the operation of the motor vehicle, and (See at least [0013]: “The driver assistance system may also include one or more environmental sensors, such as a camera, a ToF (Time of Flight) camera, a stereo camera, or similar devices, to detect lane markings. Other environmental sensors, such as radars, ultrasonic sensors, laser scanners, or similar devices, may also be provided. Such sensors can also be used to detect road boundaries, guardrails, roadside buildings, and so on, which can be used to determine the lane area, especially the second lane area. When recording lane markings, both lane markings running to the left of the vehicle in the direction of travel and lane markings running to the right of the vehicle can be recorded and used to determine the second lane route. If, for example, lane markings can only be detected from one side of the vehicle because they are only present on one side, a predetermined standard width can be assumed to calculate the lane's course, in particular its width perpendicular to the direction of travel.”)
on the basis of said data the degrees of trustworthiness are ascertained dynamically, at least partially. (See at least [0070]: “…The lane area to be predicted can now be calculated again as an average of the first and second lane areas 12, 13 for the respective distance ranges A1, A2, A3 and A4 with a weighting according to the respective reliability values W1, W2 assigned to the distance ranges A1, A2, A3, A4, and then a target selection can be carried out on this basis. This procedure is repeated, for example periodically, in successive time steps.”)
Regarding claim 15, Fradin discloses all the limitations of claim 11 as discussed above.
Fradin additionally discloses:
wherein a distance, as far as which, starting from a current position of the motor vehicle, at least one of the data sources and/or at least some of the data is/are to be used for predicting the driving path, is ascertained as a function of the environmental scenario ascertained in a given case. (See at least Fig. 7 & [0065-0066]: “…For this purpose, the area in the direction of travel in front of the vehicle 1 and accordingly also the first and second lane areas 12, 13 are divided into several adjacent specific distance areas A1, A2, A3, A4…Although the boundary lines between the respective distance ranges A1, A2, A3, A4 are illustrated as straight lines, these boundaries can in turn run, for example, on circular lines around a distinguished point of the vehicle 1, with reference to which these respective distances are measured. For the first lane area 12, corresponding reliability values W1 are then calculated for a respective distance range A1, A2, A3, A4. For a relatively fast-moving private vehicle 1, for example at 70 km/h, the reliability of the first lane area 12 in the first distance area A1 is very good, and is specifically given here as an example with 100%. With increasing distance, the reliability of the first lane area 12 decreases, here exemplified by over 75% in the second distance area A2, 50% in the third distance area A3 up to 25% in the fourth distance area A4.” See also [0010] regarding the first lane area being determined based on self-motion parameters of the vehicle and the second lane area being determined based on detected lane markings and [0018] regarding using environmental data for determining a reliability value.)
Regarding claim 16, Fradin discloses all the limitations of claim 11 as discussed above.
Fradin additionally discloses:
wherein only those data sources and/or data, the degree of trustworthiness of which corresponds to at least a predetermined minimum degree of trustworthiness, are incorporated into the amalgamation and into the prediction of the driving path. (See at least [0018]: “…For high reliability, the curvature of the lane markings, their lateral position and their orientation must also be precisely determined, so that uncertainties in determining these quantities can be included in the calculation of at least a second reliability value. All these parameters are suitable for evaluating the reliability of the second lane area calculated on the basis of the environmental data or sensor data, in particular through the corresponding at least one second reliability value. The at least one initial reliability value for the first lane area can be described primarily on the basis of dynamic parameters of the vehicle itself, and will be explained in more detail later. Furthermore, the respective reliability value can be higher the higher the reliability of the lane area being evaluated. Such a reliability value can, for example, take on a value between zero and one, or correspondingly between 0% and 100%.”)
Regarding claim 17, Fradin discloses all the limitations of claim 11 as discussed above.
Fradin additionally discloses:
wherein for at least some of the data, uncertainty thereof is ascertained in addition, and said data are also weighted in accordance with said uncertainties, so that a greater uncertainty results in a lower weighting. (See at least [0018]: “…Results of data interpretation can also be included in the second reliability value, such as whether lane markings could be detected or classified as such, the probability that the identified lane markings are actually lane markings, and so on. For high reliability, the curvature of the lane markings, their lateral position and their orientation must also be precisely determined, so that uncertainties in determining these quantities can be included in the calculation of at least a second reliability value…” & [0020]: “…in a close proximity to the vehicle, the predicted lane area is usually significantly influenced by the values of the first lane area, since in the close proximity the prediction of the lane area based on the vehicle's own movement is usually significantly better than a prediction based on environmental data. In contrast, in a distant area where a lane area calculation based on the vehicle's own movement is usually significantly less accurate than a prediction based on environmental perception, the predicted lane area is significantly influenced by the values of the calculated second lane area.”)
Regarding claim 18, Fradin discloses all the limitations of claim 11 as discussed above.
Fradin additionally discloses:
wherein objects in the respective environment of the motor vehicle that are relevant for guidance of the motor vehicle are selected based on the predicted driving path. (See at least [0019]: “…By taking the distance between the object and the vehicle into account as described above, this circumstance can be taken into account in a particularly advantageous way. This allows at least one reliability value to be determined specifically for the distance at which the object is currently located, both for the first lane area and when calculating the second lane area. This allows for a particularly accurate and reliable prediction of the lane area, especially at the location of the object. This also allows the subsequent determination of whether the object is located in the predicted lane area or not to be provided with particularly high accuracy and a low error rate…” & [0023]: “…The positions of detected objects can then be compared with the predicted lane area to determine whether one or more of the objects are located in the predicted lane area.”)
Regarding claim 19, Fradin discloses:
An assistance system for a motor vehicle, comprising: (See at least [0001]: “The invention relates to a driver assistance system for a vehicle, which is hereinafter also referred to as the own vehicle, for predicting a lane area ahead of the own vehicle in order to determine whether an object, such as a vehicle driving ahead of the own vehicle, is located in the predicted lane area…”)
an interface for capturing various data usable for predicting a driving path; (See at least [0046]: “…The environmental sensors 3 are designed to detect at least part of the vehicle's surroundings, and in particular lane markings 4 of a currently driven roadway 5. A navigation device, such as a GPS sensor and an associated map, can also be provided as an additional environmental sensor 3, for example to determine whether the vehicle 1 is currently driving on a straight road or a curved road…”)
a processor and a computer-readable data memory coupled with the interface, wherein the assistance system is configured to: (See at least [0046]: “…Furthermore, the vehicle 1, in particular the driver assistance system 2, has a control unit 7 which is designed to evaluate the detected quantities…” & [0025]: “…Navigation data, for example from a navigation device which may include a GPS receiver or other positioning device, as well as map data which may be stored in a memory…”)
ascertain a respective environmental scenario currently situated ahead of the motor vehicle in a direction of travel; (See at least Fig. 1 & [0046]: “…The environmental sensors 3 are designed to detect at least part of the vehicle's surroundings, and in particular lane markings 4 of a currently driven roadway 5. A navigation device, such as a GPS sensor and an associated map, can also be provided as an additional environmental sensor 3, for example to determine whether the vehicle 1 is currently driving on a straight road or a curved road…”)
ascertain, for the respective ascertained environmental scenario, degrees of trustworthiness of several different data sources and/or data items originating from said data sources and on the basis of which the driving path is predictable; and(See at least [0065]: “Figure 1 shows a schematic representation of a roadway 5 with a vehicle 1 and a vehicle 14 driving in front of the vehicle 1. Furthermore, the first lane area 12 calculated according to the first determination method, as well as the lane area 13 calculated according to the second determination method, are shown again, in particular for a specific time. In this example, reliability values W1, W2 can now be calculated for each lane area 12 , 13…” & [0071]: “…Furthermore, for a respective defined distance range A1, A2, A3, A4, a reliability value W1, W2 is calculated in step S41 for the respective calculated lane areas 12, 13…”. See also [0010] regarding the first lane area being determined based on self-motion parameters of the vehicle and the second lane area being determined based on detected lane markings and [0018] regarding using environmental data for determining a reliability value.)
amalgamating together several of the data items originating from the various data sources in a weighted manner in accordance with the ascertained degrees of trustworthiness; (See at least [0071]: “…Furthermore, in step S42, for each of the distance ranges A1, A2, A3, A4, the first lane area 12 and the second lane area 13 are weighted accordingly according to their respective reliability values W1, W2 and averaged” & [0015]: “…Such an averaging does not necessarily have to take into account the results, i.e. the first lane area and the second lane area, of these two determination methods in the same way, but can also be weighted, for example, so that it is possible to specifically exploit the strengths of the respective determination methods and to compensate for their weaknesses…”)
and use the amalgamated data to predict the driving path. (See at least [0071]: “…The result of this averaging is provided as a predicted lane area in step S43 and checked in step S44 to see whether the measured position of the vehicle 14 ahead lies within the predicted lane area or not. If this is the case, the vehicle 14 in front is selected as the destination in step S 45; otherwise, it is not selected in step S 46.”)
Regarding claim 20, Fradin discloses all the limitations of claim 19 as discussed above.
Fradin additionally discloses:
A motor vehicle, comprising: environmental sensorics for recording environmental data that characterize an environmental scenario situated ahead; and an assistance system according to claim 19. (See at least [0046]: “…The vehicle 1, which is hereinafter also referred to as own vehicle 1, furthermore has at least one environmental sensor, where four environmental sensors 3, such as cameras, are shown here as an example…The environmental sensors 3 are designed to detect at least part of the vehicle's surroundings, and in particular lane markings 4 of a currently driven roadway 5…”. See also [0009] regarding the driver assistance system.)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20180174459 A1 is directed to generating a driving path of a vehicle using the calculated trajectory of the preceding vehicle and the lane marking information.
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/NIKKI MARIE M MOLINA/Examiner, Art Unit 3662
/ANISS CHAD/Supervisory Patent Examiner, Art Unit 3662