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 .
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 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract idea) without significantly more.
Under Step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance, the claims are directed to a process (claim 1, a method) or a machine (claim 8, an electronic control unit) or a manufacture (claim 7, a non-transitory machine-readable storage medium), which are statutory categories.
However, evaluating claim 1, under Step 2A, Prong One, the claim is
directed to the judicial exception of an abstract idea using the grouping of a mathematical relationship/mental process. The limitations include:
calculating feature vectors from the point cloud data using a backbone, wherein the feature vectors serve as key vectors for the transformer; calculating anchor positions from the point cloud data using a sampling method; ascertaining feature vectors from the anchor positions using an encoding, wherein the feature vectors serve as object queries for the transformer; calculating attention weights for cross-attention from the object queries and a spatial structure used by the backbone; determining greatest attention weights of the transformer for each of the object queries; calculating a covariance matrix for the greatest attention weights; and calculating a determinant of the covariance matrix to obtain an attention spread.
Specifically, the claim recites: calculating feature vectors; calculating attention weights; determining greatest attention weights; calculating a covariance matrix; and calculating a determinant of the covariance matrix. These steps are mathematical operations and statistical analysis applied to data derived from point cloud inputs, which the courts consistently held as abstract mathematical concept (see Electric Power Group).
Next, Step 2A, Prong Two evaluates whether additional elements of the claim “integrate the abstract idea into a practical application” in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. The claim does not recite additional elements that integrate the judicial exception into a practical application.
This judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract ideas. As set forth in the 2019 Eligibility Guidance, 84 Fed. Reg. at 55 “merely include[ing] instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application.
Although the claim references: point cloud data, a backbone, a transformer, object queries, and detection and/or tracking, these elements are used as generic data-processing components to carry out the abstract mathematical analysis. There is no improvement to point cloud sensing or tracking technology itself, no operational use of the “attention spread”, no technical constraint beyond “using transformer”, merely saying “using a transformer with an attention model” is typically treated as generic AI infrastructure, not a particular machine, and the claimed “wherein a state of the tracked objects is stored in a feature space” describes data organization, not a technological improvement.
Therefore, the claims are directed to an abstract idea.
At Step 2B, consideration is given to additional elements that may make the abstract idea significantly more. Under Step 2B, there are no additional elements that make the claim significantly more than the abstract idea.
There is no unconventional hardware, no new training method, no control logic, and no non-generic use of the result that would amount to “significantly more”.
The limitations have been considered individually and as a whole and do not amount to significantly more than the abstract idea itself.
Dependent claims 2-6 do not add anything which would render the claimed invention a patent eligible application of the abstract idea. The claim merely extends (or narrow) the abstract idea which do not amount for "significant more" because it merely adds details to the algorithm which forms the abstract idea as discussed above.
The additional element “wherein the attention weights are calculated using a decoder of the transformer during ascertainment of result feature vectors from the object queries and the key vectors” (claim 2), merely recites a conventional implementation detail of generic transformer architecture and does not add a technical improvement or integrate the abstract idea into a practical application. Accordingly, the additional limitation does not amount to significantly more under Step 2B.
Claims 7 and 8 are rejected 35 USC § 101 for the same rationale as in
claim 1.
The additional elements of “non-transitory machine-readable storage medium on which is stored a computer program” (claim 7) and “an electronic control unit” (claim 8) are recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications. Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system (Alice Corp. Pty. Ltd. v. CLS Bank Int’l 573 U.S. __, 134 S. Ct. 2347, 110 U.S.P.Q.2d 1976 (2014)).
The limitations have been considered individually and as a whole and do not amount to significantly more than the abstract idea itself.
Examiner’s Notes
Claims 1-8 distinguish over the prior art of record. The closest prior art of record
Park et al. (US20240062520) is directed to a transformer-based perception system employing object queries and attention mechanisms for object detection and localization in spatially structured feature representations, such as grid-based feature maps and bird’s-eye-view (BEV) feature maps (see ¶¶ [0028]-[0030], [0074], [0099]-[0100] and [0147]-[0151]). Park et al. discloses extracting feature maps using a backbone network, including ResNet and transformer based backbones, wherein the resulting feature maps comprise arrays of grid cells each having associated feature vectors (see ¶¶ [0102]-[0104]). Park et al. further teaches initializing a plurality of object queries, often exceeding an expected number of objects, with the object queries organized as vectors or tensors and optionally initialized randomly or pseudo-randomly (see ¶¶ [0105]-[0107]). The object queries are iteratively refined through multiple layers of an attention stage that includes cross-attention between object queries and spatial feature cells as well as self-attention among object queries (see ¶¶ [0104], [0108]-[0113], [0118]-[0120] and [0167]), and the refined object queries are provided to BEV feature maps and detection stage to generate bounding boxes and support object identification and prediction tasks (see ¶¶ [0031] and [0147]-[0153]). In particular, Park et al. teaches associating object queries with spatial locations in feature maps or BEV feature maps via learned linear transformations that map object query tensors to grid-cell locations, and using attention weights to aggregate feature information from the identified grid cells to update object query representations (see ¶¶ [0108], [0116]-[0117] and [0150]). However, Park et al. does not disclose or suggest identifying uncertainty based on statistical properties or attention weights, determining greatest attention weights per object query, computing a covariance matrix from attention weights, or calculating a determinant of such a covariance matrix to obtain an attention spread. Rather, attention weights in Park et al. are used solely as part of soft feature aggregation for query refinement, and Park et al. does not convert attention behavior into quantitative uncertainty metric or address point-cloud based uncertainty estimation during detection and/or tracking via covariance-based analysis of attention statistics. Accordingly, although Park et al. is close with respect to transformer-based object queries and attention driven feature aggregation, Park et al. fails to disclose a method for identifying uncertainties during detection and/or tracking of multiple objects from point cloud data using a transformer with an attention model, wherein a state of the tracked objects is stored in a feature space, the method including steps of: calculating attention weights for cross-attention from the object queries and a spatial structure used by the backbone; determining greatest attention weights of the transformer for each of the object queries; calculating a covariance matrix for the greatest attention weights; and calculating a determinant of the covariance matrix to obtain an attention spread, in combination with the rest of the claim limitations as claimed and defined by the applicant.
Prior art
The prior art made record and not relied upon is considered pertinent to applicant’s
disclosure:
Yang et al. [‘216] discloses a method for processing input data with a feature extraction stage of a machine learning model to generate a feature map; applying an attention map to the feature map to generate an augmented feature map; processing the augmented feature map with a refinement stage of the machine learning model to generate a refined feature map; processing the refined feature map with a first regression stage of the machine learning model to generate multi-dimensional task output data; and processing the refined feature data with an attention stage of the machine learning model to generate an updated attention map.
Agrawal et al. [‘787] discloses receiving a detection frame and estimating (i) an overall probability of existence for a detected object and (ii) covariances for each state of at least one bounding box (e.g., centroid, heading, width, length), and then balancing/normalizing confidence values for the bounding box based on the overall probability and the state covariances (see ¶¶ [0039]-[0041] and [0054]-[0058]). It further explains that uncertainty/covariance signals are useful for downstream tracking because conventional LiDAR/point-cloud detectors (e.g., PointPillars) may provide fixed or poorly correlated confidence/covariance values, and proposes estimating covariance using approaches such as pre-NMS proposal ensembles, distance metrics, polynomial fit/lookup tables, or pre-detection covariance modeling (see ¶¶ [0033]-[0037], [0039]-[0048] and [0056]).
Contact information
Any inquiry concerning this communication or earlier communications from the
examiner should be directed to MOHAMED CHARIOUI whose telephone number is (571)272-2213. The examiner can normally be reached Monday through Friday, from 9 am to 6 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Schechter can be reached on (571) 272-2302. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Mohamed Charioui
/MOHAMED CHARIOUI/Primary Examiner, Art Unit 2857