CTNF 18/834,474 CTNF 86686 DETAILED ACTION Notice of Pre-AIA or AIA Status . 07-03-aia AIA 15-10-aia 1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. 2. Claims 1-20 filed and preliminary amended on 07/30/2024 are pending and being examined. Claims 1 and 7 are independent form. Priority 3. This application is a 371 of PCT/US2023/011898 filed on 01/30/2023, PCT/US2023/011898 has PRO 63/304,838 filed on 01/31/2022. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 4. 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. 5. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed inventions are directed to non-statutory subject matter (an abstract idea without significantly more). 5-1. Regarding independent claim 1 , the claim recites a forestry management system comprising a processor which executes steps to [1] input point cloud data into the forestry management system, [2] segment the tree from the point cloud data using unsupervised, graph-based clustering, [3] identify a metric of a tree using an algorithm, and [4] determine a trunk location of the tree. Step 1: With regard to step (1), claim 1, is directed to a forestry management system comprising a processor. The claim 1 therefore is one of statutory categories of invention, i.e., a machine and/or manufacture. Step 2A-1 : With regard to 2A-1, The elements recited in claim 1 , as drafted, under their broadest reasonable interpretation, encompass a process(es) which is/are directed to organizing human activity, can be practically performed in human mind, or falls within mathematical concepts. For example, “segment[ing] the tree from the point cloud data using unsupervised, graph-based clustering” in step [2] in the context of this claim is mathematical calculations and falls within the “ mathematical concepts ” grouping of abstract ideas. Further, each of “identify[ing] a metric of a tree using an algorithm” in step [3] and “determin[ing] a trunk location of the tree” in step [4] encompasses mental observation, evaluations, judgments, opinions, and/or activities that “can be performed in human mind, or by a human using a pen and paper”, therefore the limitation falls within the “ mental processes ” grouping of abstract ideas. Claim 1 therefore recites an abstract idea. If a claim limitation is directed to organizing human activity, can be practically performed in human mind, or falls within mathematical concepts, then the claim recites an abstract idea . See MPEP 2106.04(a)(2). Step 2A-2 : The 2019 PEG defines the phrase "integration into a practical application" to require an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception. In the instant case, the additional elements of “input[ing] point cloud data into the forestry management system” in step [1] under their broadest reasonable interpretation, are mere data gathering recited at a high level of generality, and thus are insignificant extra-solution activity . Similarly, “a processor” is recited at high level of generality and amount to no more than mere instruction to apply the exception using a generic computer. Therefore, the claim as a whole does not integrate the judicial exception into a practical application . Step 2B : As explained above, the forestry management system comprising a processor, is at best the equivalent of merely adding the words “apply it” to the judicial exception. The “input[ing] point cloud data into the forestry management system” in step [1] was considered insignificant extra-solution activity . These conclusions should be reevaluated in Step 2B. The limitations are mere data gathering and/or output recited at high level of generality and amount to receiving (i.e., acquiring), accessing, or transmitting data over a network, which is well-understood, routine, conventional activity . See MPEP 2106.05(d), subsection II. The limitations remain insignificant extra- solution activity even upon reconsideration. Even when considered in combination, the additional elements present mere instructions to apply an exception and insignificant extra-solution activity, which cannot provide an inventive concept. The claim therefore is ineligible . 5-2. Regarding dependent claims 2-6 , they are dependent from claim 1 and viewed individually, these additional elements are under its broadest reasonable interpretation, either covers performance of the limitation in the mind, performing a mathematical algorithm or extra solution activity for data gathering and do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. And, when the claims are viewed as a whole, they do not improve a technology by allowing the technology to perform a function that it previously was not capable of performing; and they do not provide any limitations beyond generally linking the use of the abstract idea to a broad technological environment (i.e., computer-based analysis of generic data). Hence, the claimed invention does not constitute significantly more than the abstract idea, so the claims are rejected under 35 USC § 101 as being directed to non-statutory subject matter. 5-3. Regarding independent claim 7 , the claims recite a method which essentially is analogous to claim 1, grounds of rejection analogous to those applied to claim 1 are applicable to claim 7. Furthermore, the claim is a method that does not recite any additional elements, and according to step 2A-2 does not integrate the abstract idea into a practical application because it does not recite any additional elements that impose any meaningful limits on practicing the abstract idea. The claim recites an abstract idea. Because the claim fails under (2A), the claim is further evaluated under (2B). The claim herein does not include any additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible. 5-4. Regarding dependent claims 8-20 they are dependent from claim 7 and viewed individually, these additional elements are under its broadest reasonable interpretation, either covers performance of the limitation in the mind, performing a mathematical algorithm or extra solution activity for data gathering and do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. And, when the claims are viewed as a whole, they do not improve a technology by allowing the technology to perform a function that it previously was not capable of performing; and they do not provide any limitations beyond generally linking the use of the abstract idea to a broad technological environment (i.e., computer-based analysis of generic data). Hence, the claimed invention does not constitute significantly more than the abstract idea, so the claims are rejected under 35 USC § 101 as being directed to non-statutory subject matter. Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 6. 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. 07-07-aia AIA 07-07 7. 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 – 07-08-aia AIA (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. 07-15 AIA 8. Claims 1- 15, and 17-20 are re jected under 35 U.S.C. 102(a) (1) a s being an ticipated by De rsch et al ( “Combining graph-cut clustering with object-based stem detection for tree segmentation in highly dense airborne lidar point clouds” , 2021, hereinafter “Dersch”). Re garding claim 1 , Dersch discloses a forestry management system comprising a processor which executes steps ( the tree detection system; see fig.1, the title and abstract ) to; input point cloud data into the forestry management system ( “highly dense 3D point cloud”; see fig.1; see fig.7 (a) ), segment the tree from the point cloud data using unsupervised, graph-based clustering ( “single tree segmentation” by the “graph-cut based clustering”; see fig.1 and Sec. 3.3. It should be noticed that the graph-cut based clustering is an unsupervised, i.e., based on k-means clustering. ), identify a metric of a tree using an algorithm, and determine a trunk location of the tree ( see sec. 6.3, paragraph 2, lines 7-11; “our procedure provides not only the segmented point cloud per tree and important tree parameter (e.g. position and height of the tree ), but also the tree trunk as 3D information, which in turn can be used to model the tree trunk as an object .”). Regarding claim 2 , Dersch discloses the forestry management system of Claim 1, wherein the metrics include at least one of a height of the tree, a biomass of the tree, a health status of the tree, and a species of the tree (see sec. 1, para.1). Regarding claim 3 , Dersch discloses the forestry management system of Claim 1, wherein the metric is a stem location and a position of the tree (e.g., see the tree stems’ locations shown in fig.7(d)). Regarding claim 4 , Dersch discloses the forestry management system of Claim 3, wherein the position of the tree is the angle of a trunk of the tree in relation to a ground surface (see “the fitted magenta lines” in fig.7(d)). Regarding claim 5 , Dersch discloses the forestry management system of Claim 1, wherein the algorithm has a canopy- to-root routing direction (see sec.2, para.2, lines 30-34: “In a first step, the tree canopy is segmented from a leaf-on point cloud to generate initial tree segments. Second, tree trunks are clustered from the point cloud, acquired in a leaf-off situation, and are subsequently used to refine the segmentation from the previous step.”). Regarding claim 6 , Dersch discloses the forestry management system of Claim 5, wherein the canopy-to-root routing direction simultaneously segments the point cloud data and discovers a stem location of the tree (ibid.). Regarding claim 7 , Dersch discloses a method of using a forestry management system, the method comprising the steps of: providing a forestry management system including a processor which executes steps to input point cloud data into the forestry management system, segment the tree from the point cloud data using unsupervised, graph-based clustering, identify a metric of a tree using an algorithm, and determine a trunk location of a tree ( see fig.1 and sec. 6.3, paragraph 2, lines 7-11 “our procedure provides not only the segmented point cloud per tree and important tree parameter (e.g. position and height of the tree), but also the tree trunk as 3D information, which in turn can be used to model the tree trunk as an object.”); preprocessing the point cloud data (see sec. 4.2, par.1 lines 9-12: “the reflectance value calculated by the software RiAnalyze © is normalized with regard to the travelling distance of the laser beam. Table 2 summarizes the parameters of the RIEGL VQ 1560i scanner.”); building a graph model ( see sec. 3.3—Graph-cut clustering ); identifying the canopy to root path of the tree ( see sec.2, para.2, lines 30-34 : “In a first step, the tree canopy is segmented from a leaf-on point cloud to generate initial tree segments. Second, tree trunks are clustered from the point cloud, acquired in a leaf-off situation, and are subsequently used to refine the segmentation from the previous step.”); segmenting the tree from remaining point cloud data; and determining the trunk location of the tree ( see sec. 6.3, paragraph 2, lines 7-11 ; “our procedure provides not only the segmented point cloud per tree and important tree parameter (e.g. position and height of the tree), but also the tree trunk as 3D information, which in turn can be used to model the tree trunk as an object.”). Regarding claim 8 , Dersch discloses the method of Claim 7, wherein the step of preprocessing the point cloud data includes normalizing the point cloud data by subtracting a terrain elevation from each point in the point cloud data (see the right up paragraph of Sec. 3.4: “the implemented solution of the graph-cut clustering method subtracts ground points from the lidar point cloud using an appropriate filtering procedure for a DTM to avoid false ground clusters.”). Regarding claim 9 , Dersch discloses the method of Claim 8, wherein the step of preprocessing the point cloud data includes identifying a plurality of voxel cells and aggregating each point within the corresponding voxel cells, thus forming a superpoint from each aggregation (see Sec. 3.3, wherein the weighted graph G=G(V,E) is created with V as the node set, E as the edge set, w ij (o i , o j ) as the symmetric, non-negative pairwise object similarity function and W ={wij(oi, oj)}i=1…N,j=1…N as the similarity matrix representing the pairwise weighted interrelationship between N primitives O = {oi}i=1…N of a set of cubic voxels or super-voxels , the latter provided by an appropriate pre-segmentation such as mean shift or k-means. See Sec. 3.3, i.e., the paragraph of the right up of the Eq(5).). Regarding claim 10 , Dersch discloses the method of Claim 9, wherein the step of preprocessing the point cloud data includes applying a point count threshold by ignoring voxels containing fewer than a predetermined number of points (see Sec. 3.1, par.1, lines 5-7: “Based on our experiences, the point density must be such that at least five points/m represent the tree trunk.”). Regarding claim 11 , Dersch discloses the method of Claim 9, wherein a ground point and a canopy point are identified from a height of the superpoints (see fig.1 and sec. 6.3, paragraph 2, lines 7-11: “our procedure provides not only the segmented point cloud per tree and important tree parameter (e.g. position and height of the tree ), but also the tree trunk as 3D information, which in turn can be used to model the tree trunk as an object.”). Regarding claim 12 , Dersch discloses the claimed invention. See Sec. 6.1, para.2: “Second, the stem detection does not need a DTM to cancel ground points. Fig. 7b shows that most of the ground points are classified in a green and blueish color corresponding to a low point probability ppt. Because we optimize the control parameter pptthres in a sensitivity analysis (see Section 5.3), we discard all points whose point probabilities are below the optimized values ppt thres = 0.44 . This procedure eliminates most of the irrelevant non-stem points. If we take a look at Fig. 7c, we can notice that several segments (in red) have been filtered out. These segments result from ground points that still remain in the point cloud after elimination using pptthres.” It should be noticed that the ppt thres here is a threshold for classifying point cloud to the non-ground points including leaves (i.e., the canopy/crown points showm by fig.7(b )) or the ground points showm by figs.(b)-(d). In other words, if a point belongs to the stem/canopy point, its point probability must be upper than or equal to the ppt thres . Likewise, If a point belongs to the groung point, its point probability must be below the ppt thres .) Regarding claim 13 , Dersch discloses the method of Claim 11, wherein the graph model is built by defining an edge between at least two superpoints (see Sec. 3.3—Graph-cut clustering: “ a weighted graph G = G(E,V) is created with V as the node set, E as the edge set, wij(oi, oj) as the symmetric, non-negative pairwise object similarity function and W ={wij(oi, oj)}, i=1…N,j=1…N as the similarity matrix representing the pairwise weighted interrelationship between N primitives O = {oi}i=1…N of a set of cubic voxels or super-voxels,...”). Regarding claim 14 , Dersch discloses the method of Claim 13, wherein the graph model is calculated from the algorithm as: N= (P, E, W) (ibid.) Regarding claim 15 , Dersch discloses the method of Claim 13, wherein the step of identifying the canopy to root path of the tree further includes identifying a cost value of the edge to the ground point (see Sec. 3.3, wherein the normalized graph-cut clustering algorithm includes minimizing the objective/cost function defined by Eq(5), which includes calculating the volume of subgraph A that is equivalent to the sum of weights of all edges ending up in cluster A, maximizes the similarity within clusters (vol(A) and vol(B)), and minimizes the similarity between the disjoint clusters A and B (cut(A, B)) . It should be noticed that clusters A and cluster B are interpreted as the non-ground points including stem point and leaves (i.e., the canopy) and ground points as shown by fig.7(b).). Regarding claim 17 , Dersch discloses the method of Claim 15, wherein the step of identifying the canopy to root path of the tree further includes identifying a least-cost route of superpoints between the canopy point and the ground point (see Sec. 3.3, wherein the normalized graph-cut clustering algorithm includes minimizing the objective/cost function defined by Eq(5), which includes calculating the volume of subgraph A that is equivalent to the sum of weights of all edges ending up in cluster A, maximizes the similarity within clusters (vol(A) and vol(B)), and simultaneously minimizes the similarity between the disjoint clusters A (i.e., the canopy) and B (i.e., the ground) (cut(A, B)) . It should be noticed that clusters A and cluster B are interpreted as the non-ground points including stem point and leaves (i.e., the canopy) and ground points as shown by fig.7(b).). Regarding claim 18 , Dersch discloses the method of Claim 7, wherein the step of identifying the canopy to root path of the tree and the step of segmenting the tree from remaining point cloud data occur simultaneously (ibid.). Regarding claim 19 , Dersch discloses the method of Claim 7, wherein the step of segmenting the tree from remaining point cloud data precedes the step of determining the trunk location of the tree (see Abstract, lines 3-8: “This paper describes a novel integrated single tree segmentation using a graph-cut clustering method that is supported by automatic stem detection. The key idea is to replace the static stopping criterion, which is usually defined by trial and error or by a sensitivity analysis, here with a query for whether a stem position has been provided by the stem detection in the remaining cluster to be partitioned . The stem detection automatically detects tree stems by identifying vertical lines based on a hierarchical classification procedure.” In other words, the single tree segmentation result in the method in Dersch is based on the stem position which has been detected by the method). Regarding claim 20 , Dersch discloses the method of Claim 7, wherein the step of segmenting the tree from remaining point cloud data and the step of determining the trunk location of the tree occur simultaneously (see Sec. 3.3, wherein the normalized graph-cut clustering algorithm includes minimizing the objective/cost function defined by Eq(5), which includes calculating the volume of subgraph A that is equivalent to the sum of weights of all edges ending up in cluster A, maximizes the similarity within clusters (vol(A) and vol(B)), and simultaneously minimizes the similarity between the disjoint clusters A (i.e., the canopy) and B (i.e., the ground) (cut(A, B)). It should be noticed that clusters A and cluster B are interpreted as the non-ground points including stem point and leaves (i.e., the canopy) and ground points as shown by fig.7(b).) . Claim Rejections - 35 USC § 103 07-20-aia AIA 9. 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 of this title, 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. 07-21-aia AIA 10. Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Dersch . Regarding claim 16 , Dersch does not explicitly disclose the claimed invention. However, Dersch discloses the similarity/distance function defined by Eq.(8), which includes calculating the Euclidian distance d k (O i , O j ). It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to appreciate that claim 16 is an obvious variation of Eq(8) and have been equally interchangeable between the similarity function defined by Eq.(8) in Dersch and the similarity function defined by claim 16. Suggestion or motivation for doing so would have been to combine the graph-cut clustering with object-based stem detection for tree segmentation as taught by Dersch, see, Abstract. Therefore, the claim is unpatentable over Dersch . Conclusion 07-96 AIA 11. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. AU 2020103026. Yancho et al, “Fine-Scale Spatial and Spectral Clustering of UAV-Acquired Digital Aerial Photogrammetric (DAP) Point Clouds for Individual Tree Crown Detection and Segmentation”, 2019. 12. Any inquiry concerning this communication or earlier communications from the examiner should be directed to RUIPING LI whose telephone number is (571)270-3376. The examiner can normally be reached 8:30am--5:30pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, HENOK SHIFERAW can be reached on (571)272-4637. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /RUIPING LI/Primary Examiner, Ph.D., Art Unit 2676 Application/Control Number: 18/834,474 Page 2 Art Unit: 2676 Application/Control Number: 18/834,474 Page 3 Art Unit: 2676 Application/Control Number: 18/834,474 Page 4 Art Unit: 2676 Application/Control Number: 18/834,474 Page 5 Art Unit: 2676 Application/Control Number: 18/834,474 Page 6 Art Unit: 2676 Application/Control Number: 18/834,474 Page 7 Art Unit: 2676 Application/Control Number: 18/834,474 Page 8 Art Unit: 2676 Application/Control Number: 18/834,474 Page 9 Art Unit: 2676 Application/Control Number: 18/834,474 Page 10 Art Unit: 2676 Application/Control Number: 18/834,474 Page 11 Art Unit: 2676 Application/Control Number: 18/834,474 Page 12 Art Unit: 2676 Application/Control Number: 18/834,474 Page 13 Art Unit: 2676 Application/Control Number: 18/834,474 Page 14 Art Unit: 2676 Application/Control Number: 18/834,474 Page 15 Art Unit: 2676 Application/Control Number: 18/834,474 Page 16 Art Unit: 2676
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