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 .
Claim Rejections – 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1 and 6 are process type claims. Claim 11 is a machine type claim. Therefore, claims 1-15 are directed to either a process, machine, manufacture or composition of matter.
As per claim 1,
2A Prong 1:
“Sampling a radar point cloud dataset to generate a mini-batch of samples from the dataset, wherein the radar point cloud dataset corresponds to a first resolution” A user mentally or with pencil and paper reviews a dataset and pulls low resolution from the dataset.
“Computing noisy data samples for each sample in the mini-batch of samples” The user mentally or with pencil and paper adds noise to the sample.
“computing a conditioning input for each of the samples in the mini-batch, wherein the conditioning input is derived from low-resolution radar point cloud samples corresponding to each sample in the mini-batch with the low-resolution samples corresponding to a second resolution which is lower than the first resolution” The user mentally or with pencil computes a conditioning input from the lower resolution samples.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“training a diffusion model”, “training the diffusion model” (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 - see MPEP 2106.05(f) – Examiner’s note: Claims contain no additional detail or limitations beyond a generic, off the shelf diffusion model.
2B: The claim does not include additional elements individually or in combination that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
“training a diffusion model”, “training the diffusion model” (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 - see MPEP 2106.05(f) – Examiner’s note: Claims contain no additional detail or limitations beyond a generic, off the shelf diffusion model.
As per claim 2-3, these claims contain additional mental steps of calculating conditioning input and are rejected for similar reasons to claim 1.
As per claims 4-5, these claims contain additional generic machine learning model aspects, and are rejected similarly to claim 1.
As per claim 6,
2A Prong 1:
“computing conditioning input from a radar point cloud corresponding to a second resolution, wherein the second resolution is lower than the first resolution” The user mentally or with pencil computes a conditioning input from the lower resolution samples.
“… produce a second sample corresponding to the first resolution and including a level of noise lower than the first level of noise” The user mentally or with pencil and paper uses the conditioning input to produce a second sample at a corresponding resolution and reduce the noise of the sample.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“Apply a trained diffusion model” (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 - see MPEP 2106.05(f) – Examiner’s note: Claims contain no additional detail or limitations beyond a generic, off the shelf diffusion model.
“receiving a first sample of a radar point cloud, the first sample corresponding to a first resolution, the first sample including a first level of noise” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
2B: The claim does not include additional elements individually or in combination that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
“Apply a trained diffusion model” (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 - see MPEP 2106.05(f) – Examiner’s note: Claims contain no additional detail or limitations beyond a generic, off the shelf diffusion model.
“receiving a first sample of a radar point cloud, the first sample corresponding to a first resolution, the first sample including a first level of noise” (MPEP 2106.05(d)(II) indicate that merely “receiving and transmitting data” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed receiving step is well-understood, routine, conventional activity is supported under Berkheimer).
As per claims 7-8, these claims contain additional mental steps of conditioning input as found in claim 6, and are rejected for similar reasons.
As per claims 9-10, these claims contain additional generic machine learning models similar to claim 6, and are rejected for similar reasons.
As per claim 11,
2A Prong 1:
“Sampling a radar point cloud dataset to generate a mini-batch of samples from the dataset, wherein the radar point cloud dataset corresponds to a first resolution” A user mentally or with pencil and paper reviews a dataset and pulls low resolution from the dataset.
“Computing noisy data samples for each sample in the mini-batch of samples” The user mentally or with pencil and paper adds noise to the sample.
“computing a conditioning input for each of the samples in the mini-batch, wherein the conditioning input is derived from low-resolution radar point cloud samples corresponding to each sample in the mini-batch with the low-resolution samples corresponding to a second resolution which is lower than the first resolution” The user mentally or with pencil computes a conditioning input from the lower resolution samples.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“one or more processors”, “memory including processor-executable instructions” (mere instructions to apply the exception using a generic computer component);
“training the diffusion model” (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 - see MPEP 2106.05(f) – Examiner’s note: Claims contain no additional detail or limitations beyond a generic, off the shelf diffusion model.
2B: The claim does not include additional elements individually or in combination that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
“one or more processors”, “memory including processor-executable instructions” (mere instructions to apply the exception using a generic computer component)
“training a diffusion model”, “training the diffusion model” (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 - see MPEP 2106.05(f) – Examiner’s note: Claims contain no additional detail or limitations beyond a generic, off the shelf diffusion model.
As per claims 12-13, these claims contain additional mental steps of calculating the conditioning input, and are rejected for similar reasons to claim 11.
As per claims 14-15, these claims contain additional generic machine learning model aspects, and are rejected for similar reasons to claim 11.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION. —The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-5 and 11-15 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “mini-batch” in claims 1 and 11 is a relative term which renders the claim indefinite. The term “mini-batch” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The specification does not establish a definition of what qualifies as a “mini-batch” when it comes to sampling the radar point clouds from a dataset as recited in these claims. The specification fails to describe or define what is meant by the term “mini-batch”. In particular, it is unclear what metrics or standards are used for ascertaining the number of samples required to qualify as a “mini-batch” in claims 1 and 11.
Claim 11 recites the limitation "training the diffusion model" in line 20. There is insufficient antecedent basis for this limitation in the claim.
Claims 2-5 and 12-15, which are dependent on claims 1 and 11, are rejected under 35 U.S.C. 112(b) as being indefinite under the same rationale as claims 1 and 11.
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 6-10 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Pangottil et al, U.S. PGPUB (US 20240386665 A1), published May 18, 2023.
With regard to independent claim 6,
Pangottil teaches “A method comprising: receiving a first sample of a radar point cloud,” (Paragraph 0037; EN: This denotes turning sensor data from image radars into point clouds). “the first sample corresponding to a first resolution,” (paragraph 0074-0081; EN: This denotes the use of coarse point clouds, which consist of low-resolution point clouds used for input and high-resolution point clouds used for ground truths). “the first sample including a first level of noise;” (paragraph 0074-0081; EN: This denotes using the sample batches from the coarse complete point clouds in a forward Gaussian process). “computing conditioning input from a radar point cloud corresponding to a second resolution,” (paragraph 0074-0081; EN: This denotes using the low-resolution point clouds from the coarse complete point clouds as the conditioning input). “wherein the second resolution is lower than the first resolution;” (paragraph 0074-0081; EN: This denotes using the low-resolution point clouds from the coarse point clouds as the conditioning input and looping through the forward Gaussian diffusion process to generate noisy outputs). “and applying a trained diffusion model to the first sample and conditioning input to produce a second sample corresponding to the first resolution and including a level of noise lower than the first level of noise” (paragraph 0074-0081; EN: This denotes the use of both a forward Gaussian process and the reverse Markov chain).
With regard to dependent claim 7,
Pangottil teaches the limitation “The method of Claim 6, wherein the conditioning input is further computed from data additional to the radar point cloud corresponding to the second resolution” (paragraph 0037, 0044, 0060; EN: This denotes turning sensor data from image radars, lidars, and RGB cameras into point clouds, which is then turned into pairs of 3D colored point clouds used as a training dataset).
With regard to dependent claim 8,
Pangottil teaches the limitation “The method of Claim 7, wherein the additional data includes an RGB image, data from an event-camera, LiDAR data, or sonar data” (paragraph 0037, 0044, 0060; EN: This denotes turning sensor data from image radars, lidars, and RGB cameras into point clouds).
With regard to dependent claim 9,
Pangottil teaches the limitation “The method of Claim 6, wherein computing the conditioning input is performed by a neural network” (paragraph 0053-0054, 0064-0071; EN: This denotes the PDR model, which includes CGNet and RFNet, using the low-resolution point clouds from the augmented training dataset as the conditioning input).
With regard to dependent claim 10,
Pangottil teaches the limitation “The method of Claim 8, wherein computing the conditioning input is performed by a neural network” (paragraph 0053-0054, 0064-0071; EN: This denotes the PDR model, which includes CGNet and RFNet, using the low-resolution point clouds from the augmented training dataset as the conditioning input).
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 1-5 and 11-15 are rejected under 35 U.S.C. 103 as being unpatentable over Pangottil et al, U.S. PG PUB (US 20240386665 A1), published May 18, 2023 in view of Karam et al., U.S. patent No. 11030485, published June 8, 2021.
With regard to independent claim 1,
Pangottil teaches “A method of training a diffusion model, comprising:” (paragraph 0031-0032; EN: This denotes training an image synthesis neural network, which can be a DDPM (denoising diffusion probabilistic model)). “sampling a radar point cloud dataset to generate a … from the dataset” (paragraph 0037, 0044, 0060, 0065; EN: This denotes turning sensor data from image radars, lidars, and RGB cameras into point clouds, turning the point clouds into pairs of 3D colored point clouds to be used as a training dataset, augmenting the training dataset and sampling a batch of inputs and ground truths from the augmented training dataset to be used in the training loop). “wherein the radar point cloud dataset corresponds to a first resolution;” (paragraph 0060, 0064-0071; EN: This denotes that the high resolution that is a part of the 3D-colored point cloud pair is used as the grounding truth). “computing noisy data samples for each sample in the …;” (paragraph 0062, 0064-0071; EN: This denotes adding Gaussian noise to the sample batch from the augmented training dataset). “computing a conditioning input for each of the samples in the …” (paragraph 0060, 0064-0071; EN: This denotes using the sample batch of low-resolution point clouds from the augmented training dataset as the conditioning input). “wherein the conditioning input is derived from low-resolution radar point cloud samples corresponding to each sample in the …” (paragraph 0060, 0064-0071; EN: This denotes using the sample batch of low-resolution point clouds from the augmented training dataset as the conditioning input). “with the low-resolution samples corresponding to a second resolution which is lower than the first resolution” (paragraph 0064-0071; EN: This denotes using the sample batch of low-resolution point clouds from the augmented training dataset as the conditioning input and looping through the forward Gaussian diffusion process to generate noisy outputs). “and training the diffusion model on the … and the conditioning input” (paragraph 0053-0054, 0064-0071; EN: This denotes that the PDR model, which is based on (DDPM) and may include a Conditional Generation Network (CGNet) and a ReFinement Network (RFNet), is trained using the batch of samples and the conditioning input).
However, Pangottil fails to explicitly disclose, “mini-batch of samples” and “mini-batch”.
Karam teaches “mini-batch of samples” (Col. 12, lines 3-9; EN: This denotes the use of 250 samples in a mini-batch). “mini-batch” (Col. 12, lines 3-9; EN: This denotes the use of a mini-batch of samples).
Pangottil and Karam are considered to be analogous art to the claimed invention
due to the fact that they both disclose the use of radar, lidar, and cameras for object detection. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the high-resolution point cloud generation of Pangottil with the generative sensing and feature regeneration framework of Karam. One would be motivated to do so to improve the accuracy and resolution of images generated by object detection and feature regeneration.
With regard to dependent claim 2,
Pangottil teaches the limitation “The method of Claim 1, wherein the conditioning input is further derived from data additional to the low-resolution radar point cloud samples” (paragraph 0037, 0044, 0060; EN: This denotes turning sensor data from image radars, lidars, and RGB cameras into point clouds, which is then turned into pairs of 3D colored point clouds used as a training dataset).
With regard to dependent claim 3,
Pangottil teaches the limitation “The method of Claim 2, wherein the additional data includes an RGB image, data from an event-camera, LiDAR data, or sonar data” (paragraph 0037, 0044, 0060; EN: This denotes turning sensor data from image radars, lidars, and RGB cameras into point clouds).
With regard to dependent claim 4,
Pangottil teaches the limitation “The method of Claim 1, wherein computing the conditioning input is performed by a neural network” (paragraph 0053-0054, 0064-0071; EN: This denotes the PDR model, which includes CGNet and RFNet, using the low-resolution point clouds from the augmented training dataset as the conditioning input).
With regard to dependent claim 5,
Pangottil teaches the limitation “The method of Claim 3, wherein computing the conditioning input is performed by a neural network” (paragraph 0053-0054, 0064-0071; EN: This denotes the PDR model, which includes CGNet and RFNet, using the low-resolution point clouds from the augmented training dataset as the conditioning input).
With regard to independent claim 11,
This claim is similar in scope to claim 1 and is rejected under a similar rationale.
With regard to dependent claim 12,
This claim is similar in scope to claim 2 and is rejected under a similar rationale.
With regard to dependent claim 13,
This claim is similar in scope to claim 3 and is rejected under a similar rationale.
With regard to dependent claim 14,
This claim is similar in scope to claim 4 and is rejected under a similar rationale.
With regard to dependent claim 15,
This claim is similar in scope to claim 5 and is rejected under a similar rationale.
Conclusion
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/SETH CAPRIANO-UMARI SHELTON/Examiner, Art Unit 2141
/BEN M RIFKIN/Primary Examiner, Art Unit 2123
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