Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 4/7/26 has been entered.
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 1-3, 11-14, 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication 2021/0383616 A1 (hereinafter Rong) in view of “AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection” by Zongdai Liu, et al. (hereinafter Liu) in view of “PerMO: Perceiving More at Once from a Single Image for Autonomous Driving” by Feixiang Lu, et al. (hereinafter Lu) in view of “DIB-R++: Learning to Predict Lighting and Material with a Hybrid Differentiable Renderer” by Wenzheng Chen, et al. (hereinafter Chen).
Regarding claim 1, the limitations “A method comprising: generating a target object model for a target object … rendering, by a differential rendering engine, [an] object image and an object mask from a target object model; performing a first comparison of … a shape at a plurality of locations in the … object image and the object mask that are on the target object to an actual image and an actual mask of the actual image; computing, by a loss function of the differential rendering engine, a loss based on the first comparison and a second comparison of the target object model with a corresponding lidar point cloud; updating the target object model by the differential rendering engine according to the loss” are taught by Rong (Rong, e.g. abstract, paragraphs 24-183, discloses a system for generating and rendering augmented autonomous driving scenes by compositing environment data collected by an autonomous vehicle with one or more 3D objects/assets stored in an object bank, e.g. paragraphs 24-57. Rong teaches details of generating the 3D objects/assets stored in the object bank, e.g. paragraphs 31-42, including using a 3D mesh model representing the shape of the object to predict images and/or shapes of a target object, e.g. paragraphs 36, 37, calculating a loss based on the difference between the predicted images and object features in the input image(s), e.g. paragraphs 34, 37, 38, and based on the difference between the predicted shape and the object features in the input LiDAR point cloud data, e.g. paragraphs 35, 37, 39, and updating the parameters of the 3D object/asset model based on the loss function, e.g. paragraph 37. Rong, e.g. paragraphs 34, 37, indicate that the first object data, cropped regions of captured images, is compared to predicted images, where Rong, e.g. paragraphs 38, 161, clarifies that the comparison based on silhouettes inferred from the 2D rendered images generated using differentiable neural rendering, i.e. the silhouette loss is the first comparison of shape at a plurality of locations in the object image used to infer the object mask which is compared to the actual mask of the target object in the actual image. That is, as claimed, the target object model, i.e. Rong’s 3D object/asset model from the object bank, is updated by rendering, by a differential rendering technique, an object image and object mask from the target object model, computing a loss function based on the first/silhouette comparison and a second comparison of the target object model with the corresponding lidar point cloud, and updating the target object model according to the loss.)
The limitation (addressed out of order) “rendering, after updating the target object model, a target object in a virtual world using the target object model” is taught by Rong (Rong, as noted above, teaches generating and rendering augmented autonomous driving scenes by compositing environment data collected by an autonomous vehicle with one or more 3D objects/assets stored in the object bank, i.e. Rong, e.g. paragraphs 27, 28, 42-54, teaches that the environment data is processed to identify locations for inserting selected objects from the object bank, followed by rendering the augmented scene comprising the collected environment data and the selected 3D objects/assets from the object bank, where the selected 3D objects/assets correspond to the target object models produced by the 3D object/asset generation technique. That is, as claimed, the augmented scene is a virtual world comprising one or more target object models which is rendered after the target object model(s) is(are) updated, i.e. generated by the 3D object/asset generation technique.)
The limitations “generating a target object model for a target object from a decomposed object model, … the decomposed object model comprising: a body component model for a body component of the target object, and a first auxiliary component model for a first auxiliary component of the target object” are not explicitly taught by Rong (Rong, e.g. paragraph 36, teaches that a 3D mesh that is parameterized as a category-specific mean shape in a canonical pose with a 3D deformation for each vertex may be used to represent the 3D objects/assets stored in the object bank, i.e. a CAD model is deformed to match the pre-recorded data in the library model of a corresponding object/asset, corresponding to the target object model. Rong does not explicitly address using target object models generated from a decomposed object model comprising body and auxiliary component models for the body and auxiliary components, respectively.) However, these limitations are taught by Liu in view of Lu (Liu, e.g. abstract, sections 1, 3-7, describes the AutoShape system, a 3D keypoint neural network for recovering the 3D bounding box of vehicle(s) captured in input images, e.g. sections 3, 4, figure 2. Liu, e.g. section 4, paragraph 1, section 5, trains the network using a set of reconstructed 3D vehicle objects having annotated ground truth 3D keypoints, where the reconstructed 3D vehicle objects are modeled using a deformable vehicle template model fit to pre-recorded data of a target vehicle. That is, analogous to the reconstructed models in Rong’s 3D object/asset bank, Liu’s reconstructed 3D vehicle objects are modeled by matching a mesh to input 2D images and 3D lidar point clouds, e.g. section 5, paragraph 1, section 5.2, figures 3, 4, where the matching of the mesh is performed by deforming a mean shape template model, e.g. section 5.1, and is guided using a loss function calculated based on differences between the deformed model, images of the deformed model rendered using a differentiable rendering function, and the pre-recorded data, e.g. section 5.2, equations 10-12. Finally, Liu, sections 5, 5.1, indicate the use of a deformable vehicle 3D model from reference 25, i.e. Lu, which is prior work by most of the same authors of the Liu reference. Lu, sections 1, 3-9, Appendix A, describes said deformable vehicle 3D model, including the deformable 3D object model, per se, section 4, where a dense 2D/3D mapping can be recovered between the deformable 3D object model and a 3D CAD model for the target vehicle, e.g. sections 5, 5.2, figure 6(b), allowing for fully reconstructing the pose, shape, and appearance of the vehicle, e.g. section 7, figures 8, 9. Further, Lu, sections 4-.4.2, Appendix A, describes the deformable vehicle 3D model, which is a decomposed model comprising 18 separate parts, e.g. figure 6(b), UV map shows the parts, and further decomposed into 2 groups, a first group, the body, having all the parts except for the tires, and a second group comprising the tires, where the body is non-rigidly deformed to represent a target object model, and the tires are separately deformed using rotation, translation, and scaling operations, i.e. rigid deformations used to avoid causing the tires to become elliptical. That is, as claimed, Lu’s decomposed object models comprise a body component model for the body component of the target object, and a first auxiliary component model for a first auxiliary component of the target object.)
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Rong’s autonomous vehicle simulation system to use Liu’s reconstructed 3D vehicle modelling technique, comprising Lu’s deformable vehicle 3D model, to generate 3D object/asset models for the 3D object/asset model bank because the resulting 3D object/asset models would have better visual reconstruction quality, e.g. Rong, paragraph 47, selects target objects in part based on having pre-recorded data at similar distances and viewpoints as it will be viewed in the simulated scene, but Lu, e.g. section 7, figure 8, teaches that the deformable vehicle 3D model can recover the full vehicle texture, making it suitable for a wider variety of simulated scenes, i.e. the fully textured model can be rendered at a wider range of novel viewpoints without loss of quality than Rong’s inverse texture warping operation technique of paragraph 47. Further it is noted that Rong’s 3D object/asset model bank is not limited to the disclosed 3D mesh reconstruction technique, e.g. paragraph 32 indicates the bank may include systems or methods, plural, such that one of ordinary skill in the art would be motivated to include additional 3D object/asset modelling techniques, as well as additional pre-recorded datasets, which as taught by Lu, could include CAD models for the target vehicles. In Rong’s modified system, Liu’s reconstructed 3D vehicle modelling technique would be used to generate deformed decomposed object models to match pre-recorded data in the object bank/library model for a target vehicle, corresponding to the claimed target object models generated from decomposed object models comprising a body component model and a first auxiliary component model.
The limitations “wherein: the body component model and the first auxiliary component model are individual and separate models, and the body component model comprises a first set of parameters comprises an identifier of the first auxiliary component model, a location parameter detailing a connection point of the first auxiliary component on the body component model, a scaling parameter detailing a scaling factor in at least one direction, and an amount of translation offset of the first auxiliary component model, and wherein generating the target object model comprises selecting the first auxiliary component model and applying scaling and rotation to the first auxiliary component model according to the first set of parameters to obtain a revised first auxiliary component model, and adding the revised first auxiliary component model to the body component model at the connection point to generate the target object model” are taught by Rong in view of Lu (Lu, Appendix A, teaches that the model is further decomposed into 2 groups, a first group, the body, having all the parts except for the tires, and a second group comprising the tires, where the body is non-rigidly deformed to represent a target object model, and the tires are separately deformed using rotation, translation, and scaling operations, i.e. rigid deformations used to avoid causing the tires to become elliptical. That is, as claimed, the decomposed object models comprise a first component model for the body component of the target object which is individual and separate from the second component model for the second tire component(s) of the target object. Further, Lu’s model comprises the additionally recited parameters, i.e. the template tire model corresponds to the auxiliary component model, and there are 4 separate sets of parameters identifying connection points of the tire model to the deformed body model, corresponding to the claimed identifier of the first auxiliary component model and a location parameter detail its connection point, where a global alignment algorithm is applied to deform each template tire model using rotation, translation, and scaling operations prior to assembly with the deformed body model, i.e. the claimed scaling and translation parameters for each first auxiliary component/connection point. Finally, as noted, Lu, Appendix A, indicates that the template tire models for each connection point are deformed by rotation, scaling, and translation, and assembled with the corresponding connection point to generate the deformed 3D model, i.e. as claimed, the target object model is generated by applying scaling and rotation to the first auxiliary component model to obtain a revised first auxiliary component model, and adding the revised first auxiliary component model to the body component model at the connection point.)
The limitations “generating a target object model for a target object from a decomposed object model, the target object model comprising a geometry model, a texture representation, and a material property representation … rendering, by a differential rendering engine, a color object image and an object mask from the target object model; performing a first comparison of a color and a shape at a plurality of locations in the color object image and the object mask that are on the target object to an actual image and an actual mask of the actual image; computing, by a loss function of the differential rendering engine, a loss based on the first comparison and a second comparison of the target object model with a corresponding lidar point cloud; updating the target object model by the differential rendering engine according to the loss, wherein the updating comprises updating each of the geometry model, the texture representation, and the material property representation of the target object model” are partially taught by Rong in view of Liu and Lu (As discussed above, Rong’s differential rendering technique uses a loss function including a first/silhouette comparison/loss between the object mask from the 2D object images generated by differential rendering and the actual mask of the target object in the actual image and a second/lidar comparison/loss between the target object model geometry and a corresponding actual lidar point cloud. Rong’s differential rendering technique is used to update the geometry model of the target object, i.e. Rong only uses the differential rendering technique for modeling/updating the shape, and although the resulting model may be rendered with texture, e.g. paragraphs 47, 50, Rong’s target object model only includes geometry recovered from the differential rendering technique, without the texture and material representation updates claimed, and furthermore Rong does not explicitly indicate that the object images are rendered in color. Further, as discussed above, in Rong’s modified system, Liu’s reconstructed 3D vehicle modelling technique would be used to generate deformed decomposed object models to match pre-recorded data in the object bank/library model for a target vehicle, corresponding to the claimed target object models generated from decomposed object models comprising a body component model and a first auxiliary component model. While Liu, e.g. section 5.2, also uses a differential rendering technique for recovering the shape of a target object model based on silhouettes/masks in the 2D rendered images, Liu does not address texture or material representation updates. Finally, Lu, e.g. section 7.2, teaches a technique for reconstruction the texture from a single viewpoint using a gradient based optimization, but not as part of a differential rendering technique, and without modeling material properties, per se.) However, this limitation is taught by Chen (Chen, e.g. abstract, sections 1, 2-7, describes DIB-R++, a hybrid differentiable rendering technique which uses a single 2D input image of a target object to construct an object model of the target object, including geometry/shape, diffuse texture, and material properties of the object surface. Chen, e.g. section 3.3, describes the two stage rendering technique wherein three families of parameters are used to encode object shape attributes, material properties, and scene illumination, and color images are rendered from the object model parameters which include a visibility mask identifying whether each pixel is occupied by the rendered object. Chen, e.g. section 3.4, figures 4, 5, indicates that the shading model includes a diffuse albedo component corresponding to the claimed texture map/representation, as well as specular albedo, surface roughness, and metalness parameters making up the BRDF, i.e. the claimed material properties. Further, Chen, e.g. section 4, equation 7, teaches that the differential rendering system uses a loss function including terms comparing the rendered color image pixels to the actual image pixels, i.e. the image loss computing the distance between the rendered and input image, in addition to a silhouette/mask comparison/loss term analogous to Rong’s silhouette loss term. Chen, e.g. section 5, figures 4-8, teaches that the differential rendering system successfully recovers 3D geometry/shape, texture, and material properties from 2D images of cars. Finally, Chen, e.g. section 7, paragraph 2, suggests the system can be used in autonomous vehicle systems like Rong’s, i.e. Rong’s system is directed to use in training autonomous vehicle systems, and further suggests, e.g. section 5.3, paragraph 4, that a more advanced shape model could predict different parameters for different regions, i.e. Chen’s suggested improvement could be accomplished by predicting different parameters for the different parts of Lu's deformable vehicle 3D model.)
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Rong’s autonomous vehicle simulation system, using Liu’s reconstructed 3D vehicle modelling technique, comprising Lu’s deformable vehicle 3D model, to use Chen’s DIB-R++ differential rendering system for Rong’s differential rendering technique in order to recover texture and material properties for the vehicles as part of the differential rendering technique instead of a post-modeling step as taught by Lu, e.g. section 7.2, and because Chen’s technique would provide visually superior results by recovering the BRDF separating diffuse and specular properties instead of only estimating the combined texture, and because, as noted above, Chen suggests the DIB-R++ can be used to improve autonomous vehicle systems. In Rong’s modified system using Chen’s DIB-R++ differential rendering system for Rong’s differential rendering technique in order to recover texture and material properties for the vehicles as part of the differential rendering technique, the 2D images generated by differential rendering would be color images, the comparison/loss function used to update the target object model would include the claimed first comparison based on color at a plurality of locations in the target object in the rendered image and the target object in the actual image, i.e. Chen’s image loss, and based on shape/object mask in the rendered image compared to the actual mask of the target object in the actual image, i.e. Rong’s silhouette loss/Chen’s mask loss, and the second comparison of the target object model with a corresponding lidar point cloud, i.e. Rong’s lidar loss, and the geometry/shape, texture, and material properties parameters of the target object model would be updated based on the comparison/loss function as taught by Chen, instead of just the geometry/shape parameters as in Rong’s unmodified differential rendering technique.
Regarding claim 2, the limitations “further comprising: obtaining an … CAD model; obtaining a library … model for the target object; deforming, by a CAD transformer engine, the … CAD model to match the library … model to generate a deformed … CAD model; [using] the deformed annotated CAD model to generate an [improved] library model, wherein the target object model is generated from the [improved] library model” are taught by Rong (Rong, e.g. paragraphs 31, 32, teaches that the object bank includes pre-recorded data for objects stored in the object bank, i.e. each 3D object/asset starts as a set of pre-recorded data, which may include 3D bounding boxes, corresponding to a library model(s) of a target object, i.e. the object bank is a library and the objects/assets initially correspond to pre-recorded data which are library model(s) for target object(s). Rong, e.g. paragraph 36, further teaches that a 3D mesh that is parameterized as a category-specific mean shape in a canonical pose with a 3D deformation for each vertex may be used to represent the 3D objects/assets stored in the object bank, i.e. a CAD model is deformed to match the pre-recorded data in the library model of a corresponding object/asset to generate a deformed library 3D model representing the target object, corresponding to obtaining and deforming a CAD model to match the library model of a target object, where the deformed CAD model is combined with the pre-existing data in the library object to generate a target object model, i.e. the target object model is generated from the improved library model comprising the deformed CAD model matching the library model data for the target object.)
The limitations “further comprising: obtaining an annotated CAD model; obtaining a library CAD model for the target object; deforming, by a CAD transformer engine, the annotated CAD model to match the library CAD model to generate a deformed annotated CAD model; annotating the library CAD model with an annotation from the deformed annotated CAD model to generate an annotated library model, wherein the target object model is generated from the annotated library model” is not explicitly taught by Rong in view of Liu and Lu (While, as noted above, Rong teaches that a CAD model is deformed to match the library model data for a target object, where the deformed CAD model is used to improve the library model from which the target object model is generated, Rong does not address whether the deformed CAD model/3D mesh is annotated, or by extension, using an annotated CAD model/3D mesh deformed to match the library model data to generate annotations for the library model. In the modification of Rong’s autonomous vehicle simulation system to use Liu’s reconstructed 3D vehicle modelling technique, comprising Lu’s deformable vehicle 3D model, as discussed in the claim 1 rejection, the target object model/deformed CAD model may be Lu’s deformable vehicle model having separate body and tire component models. While not specifically addressed in the claim 1 rejection, Liu’s reconstructed 3D vehicle modeling technique includes the claimed feature of generating annotations for the library model(s). That is, Liu, section 4, paragraph 1, section 5, paragraph 1, teaches that the resulting reconstructed 3D vehicle objects have the 3D keypoints defined on the CAD models. That is, as claimed, an annotated CAD model is deformed to match a library model of the target object to generate an deformed annotated CAD model, where the annotations of the deformed annotated CAD model are stored as part of the reconstructed 3D vehicle model in the 3D object bank/library, where the reconstructed 3D vehicle models in the 3D object bank/library are, analogous to the claimed use for generating the target vehicle model, used as rendering assets for a related system. While Liu’s reconstructed 3D vehicle modelling technique corresponds to the claimed features of annotating library models of rendering assets by deforming an annotated CAD model to match a “library CAD model of the target object from the pre-recorded data. However, Liu, sections 5, 5.1, indicate the use of Lu’s deformable vehicle 3D model as discussed in the claim 1 rejection, wherein Lu, sections 4.2, 5.1, paragraph 1, 5.2, teaches that the ApolloCar3D dataset provides, in addition to the pre-recorded image and pose data, CAD models for the respective target vehicles, which can be used directly to generate the dense 2D/3D UV mapping, i.e. the annotated deformable CAD template model is deformed to match a library CAD model as part of the reconstruction process when said library CAD model is provided in the input dataset. That is, as claimed, in Rong’s modified system, Liu’s reconstructed 3D vehicle modelling technique would be used to generate annotated library models of target vehicles by deforming an annotated CAD model to match pre-recorded data in the object bank/library model for a target vehicle, including deforming the annotated CAD model to match a pre-recorded CAD model in the object bank/library model for a target vehicle when available, as in Lu’s use of the ApolloCar3D dataset, corresponding to the claimed annotated library models generated from the deformed annotated CAD model, which are used to generate the target object models for rendering the target objects in a virtual world.)
Regarding claim 3, the limitations “generating, with a parameterization engine, the decomposed object model from the annotated library model; and storing the decomposed object model” are taught by Rong in view of Liu and Lu (As discussed in the claim 2 rejection above, in Rong’s modified system, Liu’s reconstructed 3D vehicle modelling technique would be used to generate annotated library models of target vehicles, where Liu’s reconstructed 3D vehicle modelling technique comprises Lu’s deformable vehicle 3D model. Lu, sections 4-4.2, Appendix A, describes the deformable vehicle 3D model, which is a decomposed model comprising 18 separate parts, as well as separate body and tire component models as discussed in the claim 1 rejection above. That is, as claimed, the annotated library model is stored as a decomposed object model.)
Regarding claims 12 and 19, the limitations are similar to those treated in the above rejection(s) and are met by the references as discussed in claim 1 above, with Rong, e.g. paragraphs 109-113, teaching that the system may be implemented using a processor executing a program stored in a non-transitory memory.
Regarding claims 13 and 20, the limitations are similar to those treated in the above rejection(s) and are met by the references as discussed in claim 2 above.
Regarding claim 14, the limitations are similar to those treated in the above rejection(s) and are met by the references as discussed in claim 3 above.
Claims 21 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication 2021/0383616 A1 (hereinafter Rong) in view of “AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection” by Zongdai Liu, et al. (hereinafter Liu) in view of “PerMO: Perceiving More at Once from a Single Image for Autonomous Driving” by Feixiang Lu, et al. (hereinafter Lu) in view of “DIB-R++: Learning to Predict Lighting and Material with a Hybrid Differentiable Renderer” by Wenzheng Chen, et al. (hereinafter Chen) as applied to claim 1 above, and further in view of “Generic, Deformable Models for 3-D Vehicle Surveillance” by Matthew J. Leotta (hereinafter Leotta)
Regarding claim 21, the limitations “wherein the target object is a vehicle, and wherein the body component model comprises: the first set of parameters for a first non-steered tire and a second non-steered tire, a second set of parameters for a first steered tire and a second steered tire, wherein each set of parameters identifies a corresponding connection point on the body component model, a corresponding amount of scaling, a corresponding translation offset, and wherein the second set of parameters further each comprise a yaw-relative orientation to the vehicle” are partially taught by Rong in view of Lu (As discussed in the claim 1 rejection above, Lu’s model comprises the additionally recited parameters, i.e. the template tire model corresponds to the auxiliary component model, and there are 4 separate sets of parameters identifying connection points of the tire model to the deformed body model, corresponding to the claimed identifier of the first auxiliary component model and a location parameter detail its connection point, where a global alignment algorithm is applied to deform each template tire model using rotation, translation, and scaling operations prior to assembly with the deformed body model, i.e. the claimed scaling and translation parameters for each first auxiliary component/connection point. More specifically with respect to the limitations of claim 21, said 4 separate sets of parameters correspond to the claimed first set(s) of parameters for the non-steered (rear) tires and second set(s) of parameters for the steered (front) tires, each set of parameters identifying a corresponding connection point, i.e. front left, front right, back left, or back right, an amount of scaling, translation, and rotation. None of the references, including Lu, address including a steered tire yaw-relative orientation as part of the deformed vehicle model.) However, this limitation is taught by Leotta (Leotta, e.g. abstract, chapters 1-10, discloses a system for reconstructing vehicles from surveillance cameras using a deformable 3D model, e.g. chapter 3 describes the deformable vehicle model, and chapters 4-9 describe fitting the model to surveillance video and experiments performed with the model. Leotta, e.g. sections 3.2, 3.3, teaches that the model comprises a plurality of vehicle parts, and a separate body component model and wheel component model, with the wheels being deformed separately from the body component model, e.g. page 95, final paragraph, analogous to Lu’s model. Leotta, e.g. section 10.1.4, paragraph 6, teaches that the vehicle model could be improved with a more complex motion model, accounting not only for the position of the wheels in the shape model, but also modeling the changing orientation of the wheels relative to the vehicle body, corresponding to the claimed steered tire yaw-relative orientation parameter, i.e. ground vehicles steer by rotating the steered wheels relative to the body in a yaw orientation, i.e. left and right, such that Leotta’s teaching to include a parameter modeling the orientation of the wheels relative to the body would be the claimed steered tire parameter indicating the yaw-relative orientation of the steered tire(s) to the body component of the vehicle.)
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Rong’s autonomous vehicle simulation system, using Liu’s reconstructed 3D vehicle modelling technique, comprising Lu’s deformable vehicle 3D model, using Chen’s DIB-R++ differential rendering system for Rong’s differential rendering technique, to include Leotta’s tire modelling parameter indicating the orientation of the tire(s) relative to the vehicle body because Leotta teaches that it would be interesting to improve the motion modeling in analogous deformed vehicle models used for reconstructing vehicle models from surveillance video, and further because one of ordinary skill in the art would recognize that it would increase the fidelity of Rong’s autonomous vehicle simulation, i.e. real vehicle wheels rotate relative to the vehicle body while turning, and Leotta’s improvement would allow Rong’s simulation to model the wheel-body relative rotation.
Regarding claim 22, the limitation “wherein the first auxiliary component model is for a first auxiliary component and a second auxiliary component of the target object” is taught by Rong in view of Lu (As discussed in the claim 1 rejection above, Lu’s model comprises the parameters for four tire components, i.e. the template tire model corresponds to the auxiliary component model, and there are 4 separate sets of parameters identifying connection points of the tire model to the deformed body model, corresponding to the first auxiliary component and second auxiliary component both using the first auxiliary component model.)
The limitation “and wherein the decomposed object model further comprises a second auxiliary component model” is not explicitly taught by Rong in view of Lu (None of the references, including Lu, address including a second auxiliary component model as part of the deformed vehicle model, i.e. the only additional auxiliary component model is the template tire model.) However, this limitation is taught by Leotta (Leotta, e.g. abstract, chapters 1-10, discloses a system for reconstructing vehicles from surveillance cameras using a deformable 3D model, e.g. chapter 3 describes the deformable vehicle model, and chapters 4-9 describe fitting the model to surveillance video and experiments performed with the model. Leotta, e.g. sections 3.2, 3.3, teaches that the model comprises a plurality of vehicle parts, and a separate body component model and wheel component model, with the wheels being deformed separately from the body component model, e.g. page 95, final paragraph, analogous to Lu’s model. Leotta, e.g. page 46, paragraph 2, pages 94-95, figure 5.6, teaches that in fitting the template mesh to an input CAD model, protruding structures like side mirrors, luggage racks, and wiper blades are removed, analogous to the wheel components, but teaches that the protruding structures could be modeled using separate component models disconnected from the body, just like the wheel components, i.e. Leotta teaches that the deformable vehicle model could include a first auxiliary model for the wheels/tires, analogous to Lu’s model and the claimed first/second auxiliary component as discussed above, and further second auxiliary model(s) for the protruding structures like side mirrors, luggage racks, and/or wiper blades.)
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Rong’s autonomous vehicle simulation system, using Liu’s reconstructed 3D vehicle modelling technique, comprising Lu’s deformable vehicle 3D model, using Chen’s DIB-R++ differential rendering system for Rong’s differential rendering technique, to include Leotta’s protruding part separate component models for modeling smaller protruding parts because Leotta indicates it is known to ignore these smaller parts during modeling to reduce complexity and effort, but can be modeled analogously to the wheels/tires if necessary, and one of ordinary skill in the art would recognize that modeling the protruding parts would increase the fidelity of Rong’s autonomous vehicle simulation, i.e. modeling the finer details of the vehicles results in a higher fidelity representation.
Response to Arguments
Applicant’s arguments, see page 12, filed 4/7/26, with respect to the rejection(s) of claim(s) 1-3, 11-14, and 19-22 under 35 U.S.C. 103(a) in view of Rong, Liu, Lu, and Leotta have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Rong, Liu, Lu, Chen, and Leotta.
Conclusion
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/ROBERT BADER/Primary Examiner, Art Unit 2611
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