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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. CN202210764315.8, filed on 07/01/2022.
Information Disclosure Statement
The information disclosure statement (IDS) was submitted on 08/29/2023. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: first encoding module, first projection module, second encoding module, and second projection module in Claim 7 and the extraction module, first obtaining module, a determination module, selection module in Claim 8.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. The modules are interpreted as computer processors as detailed in [0151].
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-10 are rejected under 35 U.S.C. 101. The claimed invention is directed to the abstract concept of performing abstract steps without significantly more. The claim(s) recite(s) the following abstract concepts in BOLD of
1. A data active selection and annotation method for a point cloud, comprising:
inputting initial point cloud data into a feature extraction model to extract a first feature of annotated point cloud data and a second feature of unannotated point cloud data, the initial point cloud data comprising the annotated point cloud data and the unannotated point cloud data;
inputting the unannotated point cloud data into a classification model to obtain a classification result of the unannotated point cloud data;
determining each piece of target point cloud data with a pseudo label identical to a real label from the unannotated point cloud data according to the classification result and the real label of the annotated point cloud data, the pseudo label being determined according to the classification result; and
filtering to-be-annotated point cloud data from each piece of target point cloud data according to the first feature, the second feature and the classification result of each piece of target point cloud data.
8. A data active selection and annotation apparatus for a point cloud, comprising:
an extraction module, configured to input initial point cloud data into a feature extraction model to extract a first feature of annotated point cloud data and a second feature of unannotated point cloud data, the initial point cloud data comprising the annotated point cloud data and the unannotated point cloud data;
a first obtaining module, configured to input the unannotated point cloud data into a classification model to obtain a classification result of the unannotated point cloud data;
a determination module, configured to determine each piece of target point cloud data with a pseudo label identical to a real label from the unannotated point cloud data according to the classification result and the real label of the annotated point cloud data;
a selection module, configured to filter to-be-annotated point cloud data from each piece of target point cloud data according to the first feature, a second feature and the classification result of each piece of target point cloud data.
Under step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. The above claims are considered to be in a statutory category.
Under Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitation the fall into/recite abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter that, when recited as such in a claim limitation, covers performing mathematics or mental steps.
Next, under Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application. In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
This judicial exception is not integrated into a practical application because there is no improvement to another technology or technical field; improvements to the functioning of the computer itself; a particular machine; effecting a transformation or reduction of a particular article to a different state or thing. Examiner notes that since the claimed methods and system are not tied to a particular machine or apparatus, they do not represent an improvement to another technology or technical field. Similarly there are no other meaningful limitations linking the use to a particular technological environment. Finally, there is nothing in the claims that indicates an improvement to the functioning of the computer itself or transform a particular article to a new state.
Finally, under Step 2B, we consider whether the additional elements are sufficient to amount to significantly more than the abstract idea. Claim 1 contain no additional elements. Claim 8 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the modules are interpreted under 35 U.S.C. 112(f) to be computer processors. Generic computer elements are not considered significantly more than the abstract idea and do not integrate the abstract idea into a practical application. As recited in the MPEP, 2106.05(b), merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94.
Claims 2-7 further limit the mathematical and mental processing abstract ideas without integrating the abstract concept into a practical application or including additional limitations that can be considered significantly more than the abstract idea.
The additional elements of Claim 9 of the processor and memory and of Claim 10 of the processor are considered to be generic computer elements. Generic computer elements are not considered significantly more than the abstract idea and do not integrate the abstract idea into a practical application. As recited in the MPEP, 2106.05(b), merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94.
Examiner’s Note
Claims 1-10 are not rejected under a prior art rejection (35 U.S.C. 102 or 35 U.S.C. 103).
In regards to Claims 1 and 8, Wekel (US20210063578) teaches the limitations “inputting initial point cloud data into a feature extraction model to extract a first feature of point cloud data (“In some embodiments, to form the input into the DNN, raw LiDAR detections from an environment around an ego-object—such as a moving vehicle—may be pre-processed into a format that the DNN understands. In particular, LiDAR data (e.g., raw LiDAR detections from an ordered or unordered LiDAR point cloud) may be accumulated, transformed to a single coordinate system (e.g., centered around the ego-actor), ego-motion-compensated (e.g., to a latest known position of the ego-actor), and/or projected to form a LiDAR range image” – [0025]; the DNN includes a series of convolutional and max pooling layers to facilitate image feature extraction followed by multiscale dilated convolution and up-sampling layers to facilitate global context feature extraction – [0056]; “Generally, the LiDAR data may include raw sensor data, LiDAR point cloud data, and/or reflection data processed into some other format. For example, reflection data may be combined with position and orientation data (e.g., from GNSS and IMU sensors) to form a point cloud representing detected reflections from the environment” – [0071]; “In such embodiments, there may a common trunk or stream of layers—such as feature extractor layers tasked with computing feature maps corresponding to the inputs 118 —that output and pass data to the separate heads or streams of layers of the DNN(s) 126” – [0079]);
inputting the point cloud data into a classification model to obtain a classification result of the point cloud data (Figure 7 details a flow diagram for a method of objection detection and classification using LiDAR range image, with applying to a DNN a first data representative of a LiDAR range image – [0088]); and
filtering to-be-annotated point cloud data from each piece of target point cloud data according to the first feature, the second feature and the classification result of each piece of target point cloud data (“Once in the 3D LiDAR data space, the resulting 3D LiDAR ground truth data proposals may be post-processed, in embodiments, using one or more geometric constraints. For example, geometric constraints may be imposed on the resulting 3D LiDAR ground truth data labels to filter out points that have improper or inaccurate associated labels. For example, due to differences in sensory fields of the camera and LiDAR sensor, improper alignment of the camera and the LiDAR sensor, and/or other error sources, some points may have inaccurate labels. As a result, and with an understanding that each point corresponding to a same object instance should conform to certain geometric constraints (e.g., all of the points for a pedestrian should be within two, three, five, etc. meters of one another, all points for a vehicle should be within four, five, ten, etc. meters of one another, etc.), points that fall outside of the imposed constraints may be filtered out and/or may have their associated labels updated. In some non-limiting embodiments, a random sample consensus (RANSAC) algorithm may be applied to the labels in 3D LiDAR data space to filter points out and to increase the accuracy of the resulting ground truth labels. In other embodiments, a maximum likelihood estimate sample consensus (MLESAC) algorithm, a maximum a posterior sample consensus (MAPSAC), a Hough transform algorithm, and/or another geometric constraint algorithm may be applied” – [0028]).”
Xu (Katie Xu et al., “Semantic Segmentation of sparsely annotated 3D point clouds by pseudo-labelling”, 2019 International Conference on 3D Vision, 2019, IEEE)
Teaches the limitations “inputting initial point cloud data into a feature extraction model to extract a feature of point cloud data, the initial point cloud data comprising the annotated point cloud data and the unannotated point cloud data (semi-supervised learning utilizes both labelled and unlabeled [i.e. annotated and unannotated] data in machine learning – Page 464, Section 2.2; “To facilitate feature extraction, we represent each point by its local neighborhood in its local coordinate frame. We define the neighborhood as the collection of points within a radius r of i. Using this as input to the network, we train the model as described above. This is conceptually equivalent to PointNet++ with single scale grouping [22] and we use their implementation of ball query to compute the local neighborhood. ” – Page 465, Left Column, Section 4.1); determining each piece of target point cloud data with a pseudo label identical to a real label from the unannotated point cloud data according to the classification result and the real label of the annotated point cloud data, the pseudo label being determined according to the classification result (“to address limited availability of labelled data, we adopt the pseudo-labelling process described by Lee [14] to include unlabeled data in training. The concept behind pseudo-labelling is to use the current network to make predictions and then continuing to train the network by taking select predictions as ground truth. The two main steps in this process are training, and pseudo-label selection. Lee [14] train for a set number of epochs and select pseudo labels by choosing the most confident class. After the model is retrained, a new set of pseudo-labels replaces those from the previous iteration. In our work, we take a similar approach with a few key differences.” – Page 465; Page 464 details the method in section 4 details three components of classifier, pseudo-labelling, and label selection, with the base classifier is used to model and predict point labels based on features of its local neighborhood and the pseudo labelling is applied around this base classifier to gradually label all points in the scene; Section 4.2 on Page 465 details process with convergence training, label selection, and label retention, along with a weighted cross-entropy loss function; Page 466 further details in section 5.3 the label selection process);”
Jiang (CN105701862A) teaches the limitation “inputting initial point cloud data into a feature extraction model to extract a feature of point cloud data (extracting key points of ground features based on classification from collected point cloud data – [0002]); filtering to-be-annotated point cloud data from each piece of target point cloud data according to the first feature, the second feature and the classification result of each piece of target point cloud data (filtering point cloud data according to different categories of ground feature features – [0013]; acquired raw data is subjected to data adjustment processing, optimized by performing data edge-joining processing, subjected to data matching processing – [0096]-[0100]).”
Chung (US20220206491) teaches “inputting the unannotated point cloud data into a classification model to obtain a classification result of the unannotated point cloud data (unlabeled data set input data to classifier – [0026]).”
Wekel, Xu, Jiang, and Chung are silent with regards to the language of “inputting initial point cloud data into a feature extraction model to extract a first feature of annotated point cloud data and a second feature of unannotated point cloud data, the initial point cloud data comprising the annotated point cloud data and the unannotated point cloud data."
Conclusion
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/YOSSEF KORANG-BEHESHTI/Examiner, Art Unit 2857
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