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 . Claims 1-20 are currently pending and have been examined in this application. This communication is the first action on the merits (FAOM).
Examiner's Note
Examiner has cited particular paragraphs/columns and line numbers or figures in the
references as applied to the claims below for the convenience of the applicant. Although the
specified citations are representative of the teachings in the art and are applied to the specific
limitations within the individual claim, other passages and figures may apply as well. It is
respectfully requested from the applicant, in preparing the responses, to fully consider the
references in their entirety as potentially teaching all or part of the claimed invention, as well as
the context of the passage as taught by the prior art or disclosed by the examiner. Applicant is
reminded that the Examiner is entitled to give the broadest reasonable interpretation to the
language of the claims. Furthermore, the Examiner is not limited to Applicant's definition which is not specifically set forth in the disclosure.
Information Disclosure Statement
Applicant has filed a petition to expunge the 23 NPL documents cited and provided with the information disclosure statement filed 8/12/2025. These 23 NPL documents cited have not been considered by the examiner because the documents cited therein appear to have been inadvertently filed with the application.
Claim Objections
Claims 6-7 and 17-18 are objected to because of the following informalities:
Claims 6-7 and 17-18 recite “a decoder model” but should instead recite --the decoder model--.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claims are either directed to a method, system, or non-transitory computer readable medium, which is one of the statutory categories of invention. (Step 1: YES)
The examiner has identified system claim 12 as the claim that represents the claimed invention for analysis. Claim 12 recites the limitations of:
“A system comprising: at least one processor; and a non-transitory computer readable medium for causing the at least one processor to perform operations comprising: executing an encoder model using a set of bounding box vectors and a set of point clouds to generate a set of combined feature vectors, wherein the set of combined feature vectors comprises a combined feature vector generated from a bounding box vector of the set of bounding box vectors and from a point cloud of the set of point clouds, executing an attention model using the set of combined feature vectors to generate a set of updated feature vectors, executing a decoder model using the set of updated feature vectors to generate a set of pose residuals and a size residual, updating the set of bounding box vectors with the set of pose residuals and the size residual to generate a set of refined bounding box vectors, and executing an action responsive to the set of refined bounding box vectors.”
The limitations of using a set of bounding box vectors and a set of point clouds to generate a set of combined feature vectors, using the set of combined feature vectors to generate a set of updated feature vectors, using the set of updated feature vectors to generate a set of pose residuals and a size residual, updating the set of bounding box vectors with the set of pose residuals and the size residual to generate a set of refined bounding box vectors, and executing an action responsive to the set of refined bounding box vectors, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components because they cover concepts performed in the human mind, including observation, evaluation, judgement, and opinion. Certain limitations may also be considered a mathematical process (for example determining vecotrs, pose, size, etc.,). That is, other than reciting “at least one processor; and a non-transitory computer readable medium for causing the at least one processor to perform operations…an encoder model… an attention model… a decoder model”, nothing in the claim element precludes the steps from practically being performed in the human mind. For example, but for the “at least one processor; and a non-transitory computer readable medium for causing the at least one processor to perform operations…an encoder model… an attention model… a decoder model” language, using a set of bounding box vectors and a set of point clouds to generate a set of combined feature vectors, using the set of combined feature vectors to generate a set of updated feature vectors, using the set of updated feature vectors to generate a set of pose residuals and a size residual, updating the set of bounding box vectors with the set of pose residuals and the size residual to generate a set of refined bounding box vectors, and executing an action responsive to the set of refined bounding box vectors in the context of the claim encompasses a person performing an evaluation on bounding box vectors and point clouds to identify a set of combined feature vectors. Then encompasses a person making a decision or performing an evaluation or select or otherwise refine the combined feature vectors. Then encompasses a person using the refined combined feature vectors to make an observation or evaluation and identify pose and size. Then encompasses a person using the bounding box vectors, the pose, and size to perform an evaluation to identify a set of refined bounding box vectors. Finally, the action encompasses a person selecting one of the refined bounding box vectors. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “mental processes” grouping of abstract ideas. (Step2A-Prong 1: YES. The claims are abstract)
This judicial exception is not integrated into a practical application. Limitations that are
not indicative of integration into a practical application include: (1) Adding the words "apply it"
(or an equivalent) with the judicial exception, or mere instructions to implement an abstract
idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP
2106.05.f), (2) Adding insignificant extra-solution activity to the judicial exception (MPEP
2106.05.g), (3) Generally linking the use of the judicial exception to a particular technological
environment or field of use (MPEP 2106.05.h).
In particular, the claims recite additional elements of at least one processor; and a non-transitory computer readable medium for causing the at least one processor to perform operations…an encoder model… an attention model… a decoder model. The at least one processor, non-transitory computer readable medium, encoder model, attention model, and decoder model are recited at a high-level of generality (i.e., as generic processors/memory/models performing generic computer functions) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore claim 12 is directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an "inventive concept") to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use. The additional elements claimed amount to insignificant extra-solution activities. See 2106.05(g) for more details. Generally linking the use of the judicial exception to a particular technological environment or field of use, cannot provide an inventive concept- rendering the claim patent ineligible. Thus claim 12 (and similarly claims 1 and 20) is not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Claims 2-11 and 13-19 further define the abstract idea that is present in their respective independent claims and hence are abstract for at least the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the dependent claims are directed to an abstract idea. Thus, the aforementioned claims are not patent-eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 1-2, 4-5, 8-13, 15-16, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Nezhadarya (US 2020/0082560 A1) in view of Gao (CN 112699691 A, a machine translation is attached and is being relied upon).
Regarding claim 1, Nezhadarya discloses a method comprising: executing a first model using a set of bounding box vectors and a set of point clouds to generate a set of combined feature vectors, wherein the set of combined feature vectors comprises a combined feature vector generated from a bounding box vector of the set of bounding box vectors and from a point cloud of the set of point clouds (see at least Fig. 4, [0041-0045, 0051] – The additional functions of the bounding box estimator 124 includes a mean pool function 418 which determines the mean, x, of the x coordinate values and the mean, y, of the y coordinate values for the set 402 of unordered 2D data points, thereby giving a point cloud mean, (x, y). At a subtraction function 420, the point cloud mean, (x, y), is subtracted from each of the n data points of the input set 402 of unordered 2D data points, thereby giving a set of n mean-reduced data points with 2n mean-reduced values. The additional functions of the bounding box estimator 124 also include a subtraction function 420 which passes the 2n mean-reduced values into the first neural network 410 configured for feature extraction. The output of the first neural network 410 is a feature vector 422… The extracted feature vector 422 having 1024 values.); executing a second model using the set of feature vectors to generate a set of pose residuals and a size residual (see at least Fig. 4, [0053-0054] – The extracted feature vector 422 is used as an input for the second neural network 411 for bounding box regression. The second neural network 411 includes three sub-networks 412, 414 and 416. The orientation-estimating sub-network 412 is configured to estimate the bounding box orientation angle vector [cos 2θ, sin 2θ]T 406. The size-estimating sub-network 414 is configured to estimate the bounding box size vector (w, l) 408. The center-estimating sub-network 416 is configured to determine an estimated center vector 407 of the estimated object bounding box relative to the point cloud mean, (x, y), of the point cloud.); updating the set of bounding box vectors with the set of pose residuals and the size residual to generate a set of refined bounding box vectors (see at least Fig. 4, [0042-0043, 0053-0056, 0059-0061] – The orientation-estimating sub-network 412 is configured to estimate the bounding box orientation angle vector [cos 2θ, sin 2θ]T 406. The size-estimating sub-network 414 is configured to estimate the bounding box size vector (w, l) 408. The center-estimating sub-network 416 is configured to determine an estimated center vector 407 of the estimated object bounding box relative to the point cloud mean, (x, y), of the point cloud… In the non-limiting example shown in FIG. 4, each sub-network 412, 414, 416 includes three FCL levels. A first FCL 412A in the first level of the orientation-estimating sub-network 412 has 512 nodes. Similarly, a first FCL 414A in the first level of the size-estimating sub-network 414 has 512 nodes and a first FCL 416A in the first level of the center-estimating sub-network 416 has 512 nodes. A second FCL 412B in the second level of the orientation-estimating sub-network 412 has 128 nodes. Similarly, a second FCL 414B in the second level of the size-estimating sub-network 414 has 128 nodes and a second FCL 416B in the second level of the center-estimating sub-network 416 has 128 nodes. A third FCL 412C in the third level of the orientation-estimating sub-network 412 has two nodes. Similarly, a third FCL 414C in the third level of the size-estimating sub-network 414 has 2 nodes and a third FCL 416C in the third level of the center-estimating sub-network 416 has 2 nodes.); and executing an action responsive to the set of refined bounding box vectors (see at least Fig. 4, [0039, 0042-0043, 0053-0056, 0059-0061, 0082] – The box orientation angle vector 406 and the bounding box size vector 408 are used to output the estimated bounding box vector 432 (see claim 10 below, the action comprises presenting a refined bounding box vector)… The bounding box estimator 124 provides 2D bounding box vectors that can be stored in the electronic storage 220 and used by the data analysis system 120 or the path planning system 130… Bounding box estimation has been described in the foregoing in the context of path planning for autonomous vehicles. An estimation of a bounding box for each object in an environment allows an autonomous vehicle to make path planning decisions. The autonomous vehicle benefits from information determined regarding location and size of the objects so that optimal path planning decisions can be made (see claim 11 below, the action comprises updating a course of a vehicle)).
Nezhadarya does not appear to explicitly disclose wherein the first model is an encoder model; executing an attention model using the set of combined feature vectors to generate a set of updated feature vectors; wherein the second model is a decoder model using the set of updated feature vectors.
Gao, in the same field of endeavor, teaches the following limitations: an encoder model (see at least [0043] - encoder); executing an attention model using the set of combined feature vectors to generate a set of updated feature vectors (see at least [0054] – feature vectors… using the attention model, a vector containing richer original text information can be output); executing a decoder model using the set of updated feature vectors (see at least [0043] - decoder).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have incorporated the teachings of Zou into the invention of Nezhadarya with a reasonable expectation of success for the purpose of enabling the translation generated by the decoder to more accurately express the meaning of the original text, improving the accuracy of the translation model in translating the original text (Gao – [0054]).
Regarding claim 2, Nezhadarya discloses wherein executing the first model further comprises: executing a box model of the first model using the bounding box vector to generate a box feature vector, wherein the box model comprises a perceptron model (see at least Fig. 4, [0041-0045, 0051] – The output of the first neural network 410 is a feature vector 422… … In the embodiment illustrated in FIG. 4, the first neural network 410 configured for feature extraction includes a plurality of successive multi-layer perceptrons (MLP) 424: a first MLP 424A; a second MLP 424B; and a third MLP 424C. The plurality of successive MLPs 424 is followed by a maximum pooling function 426.).
Nezhadarya does not appear to explicitly disclose wherein the first model is an encoder model.
Gao, in the same field of endeavor, teaches the following limitations: an encoder model (see at least [0043] - encoder).
The motivation to combine Nezhadarya and Gao is the same as in the rejection of claim 1 above.
Regarding claim 4, Nezhadarya discloses wherein executing the first model further comprises: executing a combination model of the first model using a box feature vector and a cloud feature vector to generate a combined feature vector of the set of combined feature vector (see at least Fig. 4, [0041-0045, 0051] – The output of the first neural network 410 is a feature vector 422… … In the embodiment illustrated in FIG. 4, the first neural network 410 configured for feature extraction includes a plurality of successive multi-layer perceptrons (MLP) 424: a first MLP 424A; a second MLP 424B; and a third MLP 424C. The plurality of successive MLPs 424 is followed by a maximum pooling function 426.).
Nezhadarya does not appear to explicitly disclose wherein the first model is an encoder model.
Gao, in the same field of endeavor, teaches the following limitations: an encoder model (see at least [0043] - encoder).
The motivation to combine Nezhadarya and Gao is the same as in the rejection of claim 1 above.
Regarding claim 5, Nezhadarya does not appear to explicitly disclose wherein executing the attention model further comprises: executing an attention layer of the attention model to perform one or more transformations to the set of combined feature vectors to generate an updated feature vector of the set of updated feature vectors; and executing a subsequent attention layer using an output from the attention layer to generate the set of updated feature vectors.
Gao, in the same field of endeavor, teaches the following limitations: wherein executing the attention model further comprises: executing an attention layer of the attention model to perform one or more transformations to the set of combined feature vectors to generate an updated feature vector of the set of updated feature vectors; and executing a subsequent attention layer using an output from the attention layer to generate the set of updated feature vectors (see at least [0054] – the inter-layer attention model between multiple feature extraction layers of the encoder is obtained… the translation model is generated based on the inter-layer attention model and the attention model of the encoder and decoder… using the attention model, a vector containing richer original text information can be output).
The motivation to combine Nezhadarya and Gao is the same as in the rejection of claim 1 above.
Regarding claim 8, Nezhadarya discloses wherein updating the set of bounding box vectors comprises: generating the refined bounding box vector by combining a pose residual of the set of pose residuals and the size residual with the bounding box vector (see at least Fig. 4, [0042-0043, 0053-0056, 0059-0061] – The box orientation angle vector 406 and the bounding box size vector 408 are used to output the estimated bounding box vector 432.).
Regarding claim 9, Nezhadarya discloses further comprising: training a trajectory refinement model, which is a machine learning model (see at least Figs. 3-4, [0029, 0039] – data analysis system 120… machine learning algorithms… bounding box estimator 124) comprising the first model and the second model to generate the set of pose residuals and the size residual from the set of bounding box vectors and the set of point clouds using training data (see at least Fig. 4, [0040, 0059-0060, 0065, 0067-0068, 0070, 0074, 0081] – networks 410, 412, 414, 416 of the bounding box estimator 124 are trained together using a test data set… to generate a validation data set), wherein the training comprises: executing the trajectory refinement model using the training data to create training output (see at least Fig. 4, [0040, 0059-0060, 0065, 0067-0068, 0070, 0074, 0081] – networks 410, 412, 414, 416 of the bounding box estimator 124 are trained together using a test data set… to generate a validation data set), executing a loss function using the training output to generate training updates, and combining the training updates with the trajectory refinement model to update the trajectory refinement model (see at least Fig. 4, [0067-0074] – an overall loss function for use in training the first and second neural networks 411 of the bounding box estimator 410, 411).
Nezhadarya does not appear to explicitly disclose wherein the first model is an encoder model; the attention model; wherein the second model is a decoder model.
Gao, in the same field of endeavor, teaches the following limitations: an encoder model (see at least [0043] - encoder); the attention model (see at least [0054] – feature vectors… using the attention model, a vector containing richer original text information can be output); a decoder model (see at least [0043] - decoder).
The motivation to combine Nezhadarya and Gao is the same as in the rejection of claim 1 above.
Regarding claim 10, Nezhadarya discloses wherein executing the action further comprises: presenting a refined bounding box vector of the set of refined bounding box vectors (see at least Fig. 4, [0039, 0042-0043, 0053-0056, 0059-0061] – The box orientation angle vector 406 and the bounding box size vector 408 are used to output the estimated bounding box vector 432 (see claim 10 below, the action comprises presenting a refined bounding box vector.).
Regarding claim 11, Nezhadarya discloses wherein executing the action further comprises: updating a course of a vehicle using the set of refined bounding box vectors (see at least Fig. 4, [0039, 0082-0083] – The bounding box estimator 124 provides 2D bounding box vectors that can be stored in the electronic storage 220 and used by the data analysis system 120 or the path planning system 130… Bounding box estimation has been described in the foregoing in the context of path planning for autonomous vehicles. An estimation of a bounding box for each object in an environment allows an autonomous vehicle to make path planning decisions. The autonomous vehicle benefits from information determined regarding location and size of the objects so that optimal path planning decisions can be made.).
Regarding claims 12 and 20, all the limitations have been analyzed in view of claim 1, and it has been determined that claims 12 and 20 do not teach or define any new limitations beyond those previously recited in claim 1; therefore, claims 12 and 20 are also rejected over the same rationale as claim 1.
Regarding claim 13, all the limitations have been analyzed in view of claim 2, and it has been determined that claim 13 does not teach or define any new limitations beyond those previously recited in claim 2; therefore, claim 13 is also rejected over the same rationale as claim 2.
Regarding claim 15, all the limitations have been analyzed in view of claim 4, and it has been determined that claim 15 does not teach or define any new limitations beyond those previously recited in claim 4; therefore, claim 15 is also rejected over the same rationale as claim 4.
Regarding claim 16, all the limitations have been analyzed in view of claim 5, and it has been determined that claim 16 does not teach or define any new limitations beyond those previously recited in claim 5; therefore, claim 16 is also rejected over the same rationale as claim 5.
Regarding claim 19, all the limitations have been analyzed in view of claim 8, and it has been determined that claim 19 does not teach or define any new limitations beyond those previously recited in claim 8; therefore, claim 19 is also rejected over the same rationale as claim 8.
Claims 3 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Nezhadarya in view of Gao and Ackerson (US 2023/0281955 A1).
Regarding claim 3, Nezhadarya does not appear to explicitly disclose wherein executing the encoder model further comprises: executing a point cloud encoder model of the encoder model using the point cloud to generate a point cloud feature vector, wherein the point cloud encoder model comprises a convolutional neural network.
Ackerson, in the same field of endeavor, teaches the following limitations: wherein executing the encoder model further comprises: executing a point cloud encoder model of the encoder model using the point cloud to generate a point cloud feature, wherein the point cloud encoder model comprises a convolutional neural network (see at least [0206] - An encoder-decoder architecture, such as U-Net or PointCNN may be used. A U-Net or Point CNN architecture may comprise an encoder that maps the input point cloud to a low-dimensional feature space and a decoder that maps the features back to a reconstructed point cloud.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have incorporated the teachings of Ackerson into the invention of Nezhadarya with a reasonable expectation of success for the purpose of reconstructing missing or corrupted parts of the a 3D point cloud and for better capturing local and global structures (Ackerson – [0206]).
Regarding claim 14, all the limitations have been analyzed in view of claim 3, and it has been determined that claim 14 does not teach or define any new limitations beyond those previously recited in claim 3; therefore, claim 14 is also rejected over the same rationale as claim 3.
Claims 6-7 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Nezhadarya in view of Gao and An (CN 114155406 A, a translation of which is attached and is being relied upon).
Regarding claim 6, Nezhadarya discloses wherein executing a second model further comprises: executing a pose residual model of the second model using an updated feature vector of the set of updated feature vectors to generate a pose residual corresponding to the bounding box vector (see at least Fig. 4, [0042-0043, 0053-0056, 0059-0061] – The orientation-estimating sub-network 412 is configured to estimate the bounding box orientation angle vector [cos 2θ, sin 2θ]T 406. The size-estimating sub-network 414 is configured to estimate the bounding box size vector (w, l) 408. The center-estimating sub-network 416 is configured to determine an estimated center vector 407 of the estimated object bounding box relative to the point cloud mean, (x, y), of the point cloud… In the non-limiting example shown in FIG. 4, each sub-network 412, 414, 416 includes three FCL levels. A first FCL 412A in the first level of the orientation-estimating sub-network 412 has 512 nodes. Similarly, a first FCL 414A in the first level of the size-estimating sub-network 414 has 512 nodes and a first FCL 416A in the first level of the center-estimating sub-network 416 has 512 nodes. A second FCL 412B in the second level of the orientation-estimating sub-network 412 has 128 nodes. Similarly, a second FCL 414B in the second level of the size-estimating sub-network 414 has 128 nodes and a second FCL 416B in the second level of the center-estimating sub-network 416 has 128 nodes. A third FCL 412C in the third level of the orientation-estimating sub-network 412 has two nodes. Similarly, a third FCL 414C in the third level of the size-estimating sub-network 414 has 2 nodes and a third FCL 416C in the third level of the center-estimating sub-network 416 has 2 nodes.).
Nezhadarya does not appear to explicitly disclose wherein the second model is a decoder model; wherein the pose residual model comprises a perceptron model.
Gao, in the same field of endeavor, teaches the following limitations: a decoder model (see at least [0043] - decoder).
The motivation to combine Nezhadarya and Gao is the same as in the rejection of claim 1 above.
An, in the same field of endeavor, teaches the following limitations: wherein the pose residual model comprises a perceptron model (see at least [0013, 0015, 0047] – multilayer perceptron to generate 3D rotation prediction).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have incorporated the teachings of An into the invention of Nezhadarya with a reasonable expectation of success for the purpose of improving accuracy of translation predictions and rotation predictions (An – [0007]). With regards to the perceptron model (also known as multilayer perceptron or MLP), Nezhadarya already teaches the use of perceptron models (see Nezhadarya – Fig. 4 and [0045-0051]). Perceptron models serve as the fundamental building blocks for neural networks to process information within each part of the sequence. Therefore, the use of a perceptron model is considered generally obvious in the field of neural networks, and could be integrated into the decoder with a reasonable expectation of success.
Regarding claim 7, Nezhadarya discloses wherein executing a second model further comprises: executing a size model using the set of updated feature vectors to generate the size residual (see at least Fig. 4, [0042-0043, 0053-0056, 0059-0061] – The size-estimating sub-network 414 is configured to estimate the bounding box size vector (w, l) 408… In the non-limiting example shown in FIG. 4, each sub-network 412, 414, 416 includes three FCL levels. A first FCL 414A in the first level of the size-estimating sub-network 414 has 512 nodes. A second FCL 414B in the second level of the size-estimating sub-network 414 has 128 nodes. A third FCL 414C in the third level of the size-estimating sub-network 414 has 2 nodes.).
Nezhadarya does not appear to explicitly disclose wherein the second model is a decoder model; wherein the size model is a size decoder model, wherein the size decoder model comprises a mean pooling layer and a perceptron model.
Gao, in the same field of endeavor, teaches the following limitations: a decoder model (see at least [0043] - decoder).
The motivation to combine Nezhadarya and Gao is the same as in the rejection of claim 1 above.
An, in the same field of endeavor, teaches the following limitations: a mean pooling layer and a perceptron model (see at least [0017, 0047] – average pooling layer… multilayer perceptron (MLP)).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have incorporated the teachings of An into the invention of Nezhadarya with a reasonable expectation of success for the purpose of improving accuracy of translation predictions and rotation predictions (An – [0007]). With regards to the mean pooling layer (also known as average pooling or GAP), Nezhadarya already teaches the use of mean pooling (see Nezhadarya – Fig. 4 and [0044, 0077]) to determine mean values. Since Nezhadarya’s size-estimating sub-network estimates the bounding box size vector, implementing a mean pooling layer would have been generally obvious since it is known that mean pooling is used for dimensionality reduction. The use of a mean pooling layer could be integrated into the decoder with a reasonable expectation of success. With regards to the perceptron model (also known as multilayer perceptron or MLP), Nezhadarya already teaches the use of perceptron models (see Nezhadarya – Fig. 4 and [0045-0051]). Perceptron models serve as the fundamental building blocks for neural networks to process information within each part of the sequence. Therefore, the use of a perceptron model is considered generally obvious in the field of neural networks, and could be integrated into the decoder with a reasonable expectation of success.
Regarding claim 17, all the limitations have been analyzed in view of claim 6, and it has been determined that claim 17 does not teach or define any new limitations beyond those previously recited in claim 6; therefore, claim 17 is also rejected over the same rationale as claim 6.
Regarding claim 18, all the limitations have been analyzed in view of claim 7, and it has been determined that claim 18 does not teach or define any new limitations beyond those previously recited in claim 7; therefore, claim 18 is also rejected over the same rationale as claim 7.
Conclusion
The prior art made of record, and not relied upon, considered pertinent to applicant’s disclosure or directed to the state of art is listed on the enclosed PTO-982. The following is a brief description for relevant prior art that was cited but not applied:
Shi (US 2021/0082181 A1) is directed to a method and apparatus for object detection, an electronic device and a computer storage medium. The method includes: acquiring three-dimensional (3D) point cloud data; determining point cloud semantic features corresponding to the 3D point cloud data according to the 3D point cloud data; determining part location information of foreground points based on the point cloud semantic features; extracting at least one initial 3D bounding box based on the point cloud data; and determining a 3D bounding box for an object according to the point cloud semantic features corresponding to the point cloud data, the part location information of the foreground points and the at least one initial 3D bounding box.
Zhang (US 2022/0301229 A1) is directed to a point cloud coding method includes obtaining description information of a bounding box size of a to-be-encoded point cloud and a normal axis of a to-be-encoded patch in the to-be-encoded point cloud, where the description information of the bounding box size of the to-be-encoded point cloud and the normal axis of the to-be-encoded patch are used to determine a tangent axis of the to-be-encoded patch and a bitangent axis of the to-be-encoded patch, and encoding a syntax element into a bitstream, where the syntax element includes an index of the normal axis of the to-be-encoded patch and information for indicating the description information of the bounding box size of the to-be-encoded point cloud, and the syntax element is used to indicate the tangent axis of the to-be-encoded patch and the bitangent axis of the to-be-encoded patch.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CAITLIN MCCLEARY whose telephone number is (703)756-1674. The examiner can normally be reached Monday - Friday 10:00 am - 7:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Navid Z Mehdizadeh can be reached at (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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/C.R.M./Examiner, Art Unit 3669
/NAVID Z. MEHDIZADEH/Supervisory Patent Examiner, Art Unit 3669