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 .
Status of Claims
Claims 1-10 are currently pending and have been examined in this application. This communication is the first action on the merits.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy of the foreign priority application DE 10 2024 124 754.1 has been received.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/28/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 1 is objected to because of the following informalities: “associating a degree of passability with the known relevant objects, wherein the degree of passability is stored in a control unit;” should be “associating the degree of passability with the known relevant objects, wherein the degree of passability is stored in the control unit;”. Appropriate correction is required.
Drawing Objections
The drawing submitted on 08/28/2025 is objected to for not being numbered it is merely labeled as “Figure” and should be “Figure 1” for compliance. Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
In claims 1 and 9, the “computing unit” invokes 112(f) as unit is a term that does not have definite structure which enables the computing unit to optically identify/classify/associate objects or control a driving operation.
In claims 1 and 9, the “control unit” invokes 112(f) as unit is a term that does not have definite structure which enables the control unit to store or control the driving operation of the vehicle.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
A review of the specification shows that the following appears to be the corresponding structure described in the specification to these claim limitations:
“[0017] The mentioned control units or control devices or control modules of the assistance system can comprise a data processing device or a processor microprocessor and/or at least one microcontroller… The program code can be stored in a data memory of the processor device.”
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f).
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.
Claims 1-6 and 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over Lopez et al. (DE 10 2017 203 654 A1), hereinafter Lopez, in view of Romain (US 2021/0390351 Al), hereinafter Romain.
With respect to claims 1, 9, and 10, Lopez discloses a method for controlling a driving operation of a vehicle, the method comprising: capturing sensor data of a monitoring area in front of the vehicle in a driving direction by way of at least one sensor of the vehicle; (see at least [0029] “The acquisition of the ego position data…by localizing the vehicle using landmarks that are detected by sensors of the vehicle”)
optically capturing environmental images of an environment of the vehicle by way of at least one camera of the vehicle; (see at least [0029] “The acquisition of the ego position data is carried out… by other environmental data that are captured, for example, by means of a stereo camera, a camera or a laser scanner (Lidar).”)
optically identifying relevant objects in front of the vehicle in the driving direction based on the sensor data and the environmental images; (see at least [0090] “the system first uses the recorded environmental data to identify elements of the traffic infrastructure in the vicinity of the vehicle, or for the section of road for which a passability assessment is to be generated.”)
classifying the relevant objects based on the sensor data and/or based on a picture of one or more of the relevant objects into known relevant objects and into unknown relevant objects; (see at least [0090-0091] “the system first uses the recorded environmental data to identify elements of the traffic infrastructure in the vicinity of the vehicle…The data recorded and, if necessary, pre-processed in steps S1 to S4 exhibit certain uncertainties… inaccuracies of the recorded map data and/or in steps for the detection, in particular of signs and traffic lights 13.”)
further processing the unknown relevant objects by inputting (see at least [0100] “This means that a probability distribution is determined which indicates the probability that a traffic sign of a certain category was assigned to the vehicle.” [0112] “a passability assessment for the road section is generated based on the previously determined probabilities or washing unit distributions.”)
identifying the unknown relevant objects and associating the degree of passability for the unknown relevant objects by the computing unit and providing the degree of passability for the unknown relevant objects to the control unit, (see at least [0091] “In the diagram shown, this is indicated by probability values, which represent an uncertainty of the traffic sign recognition… and an uncertainty in the traffic light recognition” [0112] “a passability assessment for the road section is generated based on the previously determined probabilities or washing unit distributions… the passability assessment is output by transmitting a signal to the navigation unit 7, which determines a safe driving maneuver for the situation”)
associating a degree of passability with the known relevant objects, wherein the degree of passability is stored in a control unit; (see at least [0015] “Based on the analysis of the environmental data and the determination of the traffic regulations relevant to the vehicle and the road section, a passability assessment is generated and subsequently output.”)
and controlling the driving operation of the vehicle by the control unit using the degree of passability of the known relevant objects and the unknown relevant objects. (see at least [0112] “a passability assessment for the road section is generated based on the previously determined probabilities or washing unit distributions… the passability assessment is output by transmitting a signal to the navigation unit 7, which… generates control signals so that the longitudinal and lateral guidance of the vehicle is controlled accordingly.”)
Lopez discloses the use of environmental data to identify surrounding objects and determine a passability for an automatically driven vehicle using a certainty and confidence, but does not explicitly disclose the classification of the unknown/uncertain object identification by distribution of image data of a single object.
However, Romain teaches further processing the unknown relevant objects by inputting a picture of each of the unknown relevant objects into a computing unit that estimates the degree of passability of objects, (see at least [0038] “The segmented data 320 is, in general, a portion of the original sensor data 250 associated with a single object depicted in the sensor data 250. While the segmented data 320 is discussed in relation to a single object, it should be appreciated that the model 260 may produce a separate set of segmented data for each object represented in the sensor data 250.”)
As both rely on identification of road objects to determine control of an autonomous vehicle, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the passability determination based on elements of a road area of Lopez to include the object identification and classification disclosed in Romain, with reasonable expectation of success. The motivation for doing so would have been to use an ontological detector to further associate the object with a known class in an instance where a class for an object is unknown, thereby improving further predictions and/or responses associated with the unknown object, see Romain [0007].
With respect to claim 2, Lopez discloses the use of environmental data to identify surrounding objects and determine a passability for an automatically driven vehicle using a certainty and confidence, but does not explicitly disclose the use of point cloud generation.
However, Romain teaches generating point clouds representing objects by the at least one sensor of the vehicle as the sensor data or based on the sensor data. (see at least [0033] “the segmentation model 260 implements a clustering routine where the sensor data 250 is a three-dimensional point cloud… segment data from the sensor data 250 associated with each object.”)
As both rely on identification of road objects to determine control of an autonomous vehicle, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the passability determination based on elements of a road area of Lopez to include the object identification and classification disclosed in Romain, with reasonable expectation of success. The motivation for doing so would have been to use an ontological detector to further associate the object with a known class in an instance where a class for an object is unknown, thereby improving further predictions and/or responses associated with the unknown object, see Romain [0007].
With respect to claim 3, Lopez discloses the computing unit is configured with a number of model parameters characterizing the degree of passability of objects. (see at least [0098-0099] “the road section whose passability is to be assessed… is achieved using a machine learning method, whereby various methods known per se can be used…various parameters can be used that describe the control situation independently of the actual procedure for determining the passability assessment”)
With respect to claim 4, Lopez discloses the computing unit adopts, from a master unit comprising a plurality of model parameters, a processing procedure of the master unit in determining the degree of passability of objects and the computing unit applies the processing procedure in the further processing of the unknown relevant objects. (see at least [0022] “The confidence parameter can be determined in different ways, using environmental data to describe the control situation and to determine the confidence parameter. Furthermore, any other data can be taken into account, for example by receiving pre-processed data from an external vehicle facility, for example by providing a trust parameter for a specific road section via a central server or by providing data for its determination.”)
With respect to claim 5, Lopez discloses the use of environmental data to identify surrounding objects and determine a passability for an automatically driven vehicle using machine learning, but does not explicitly disclose the classification of the unknown/uncertain object identification by distribution of image data via a master database.
However, Romain teaches in the further processing of the unknown relevant objects by the computing unit, only the picture of each of the unknown relevant objects is input into the computing unit configured with the model parameters characterizing the degree of passability of objects and imitating the processing procedure of the master unit. (see at least [0033] “The segmentation model 260 may provide the segmented data as an output or may simply label or otherwise mark (e.g., with a bounding box) a portion of the sensor data 250 associated with each different object.” [0061] “That is, the identification system 170 may train the detector 270 locally, or remotely via one or more cloud-based resources (e.g, cloud-computing environment).”)
As both rely on identification of road objects to determine control of an autonomous vehicle, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the passability determination based on elements of a road area of Lopez to include the object identification and classification disclosed in Romain, with reasonable expectation of success. The motivation for doing so would have been to use an ontological detector to further associate the object with a known class in an instance where a class for an object is unknown, thereby improving further predictions and/or responses associated with the unknown object, see Romain [0007].
With respect to claim 6, Lopez discloses the use of environmental data to identify surrounding objects and determine a passability for an automatically driven vehicle using a certainty and confidence, but does not explicitly disclose the classification of the unknown/uncertain object identification by distribution of image data of a single object.
However, Romain teaches in the further processing of the unknown relevant objects, a uniform and non-divided picture of each of the unknown relevant objects is input into the computing unit. (see at least [0038] “The segmented data 320 is, in general, a portion of the original sensor data 250 associated with a single object depicted in the sensor data 250. While the segmented data 320 is discussed in relation to a single object, it should be appreciated that the model 260 may produce a separate set of segmented data for each object represented in the sensor data 250.”)
As both rely on identification of road objects to determine control of an autonomous vehicle, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the passability determination based on elements of a road area of Lopez to include the object identification and classification disclosed in Romain, with reasonable expectation of success. The motivation for doing so would have been to use an ontological detector to further associate the object with a known class in an instance where a class for an object is unknown, thereby improving further predictions and/or responses associated with the unknown object, see Romain [0007].
With respect to claim 8, Lopez discloses the degree of passability associated with each relevant object of the relevant objects indicates whether the relevant object is passable or not passable. (see at least [0100] “This means that a probability distribution is determined which indicates the probability that a traffic sign of a certain category was assigned to the vehicle.” [0105] “Different traffic light phases can be assigned to the same traffic regulation, which classifies the passability of an intersection.”)
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Lopez in view of Romain as applied to claim 1 above, and further in view of Strauss et al. (US 2011/0044507 A1), hereinafter Strauss.
With respect to claim 7, Lopez discloses the use of environmental data to identify surrounding objects and determine a passability for an automatically driven vehicle, but does not explicitly disclose the control being to avoid a collision.
However, Strauss teaches the driving operation of the vehicle is controlled by the control unit by using the degree of passability of the known relevant objects and the unknown relevant objects, and wherein the driving operation includes an evasive maneuver of the vehicle and/or a parking operation of the vehicle. (see at least [0039] “objects of predetermined object categories may be identified within the camera images and their relative position with regard to the vehicle 101 may be determined. These may be objects, which represent a source of danger for the vehicle 101, such as objects with which the vehicle 101 could collide.” [0037] “a collision avoidance device 206 may be provided, which takes measures by driving components of the vehicle 101 to avoid collisions between the vehicle 101 and surrounding objects and/or to reduce collision consequences.”)
As both rely on identification of road objects to determine control of an autonomous vehicle, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the passability determination based on elements of a road area of Lopez to include the object identification and classification for collision avoidance disclosed in Strauss, with reasonable expectation of success. The motivation for doing so would have been to use the classification of surrounding objects to avoid or reduce collision consequences, see Strauss [0037].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Altinger et al. (US 2017/0308092 A1) discloses at least one camera of a motor vehicle is evaluated which objects can be classified as an obstacle or not an obstacle by means of at least one classifier.
Buerkle et al. (US 2024/0428560 A1) discloses detecting a dynamic object and classifying the first point as dynamic or static based on a comparison of the first point density and the one or more second point densities.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHELLEY MARIE OSTERHOUT whose telephone number is (703)756-1595. The examiner can normally be reached Mon to Fri 8:30 AM - 5:30 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Navid Mehdizadeh can be reached on (571) 272-7691. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/S.M.O./Examiner, Art Unit 3669
/NAVID Z. MEHDIZADEH/Supervisory Patent Examiner, Art Unit 3669
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