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 § 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 4-5, 9-10, 14-15 are 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.
In claims 4-5, 9-10, and 14-15, limitations such as “the NFT” and “the historical information” have no antecedent basis in respective parent claims 1, 6 and 11. But antecedent basis do appear in claims 2, 7 and 12. Examiners take claims 4-5, 9-10 and 14-15 having incorrect dependency, wherein claims 4-5, 9-10, and 14-15 should depend from claims 2, 7 and 12. For clarity of interpretation, said indefinite claims are not considered for further examination.
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.
Claim(s) 1, 3, 6, 8, 11, 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gong et al. (“Individual Tree AGB Estimation of Malania oleifera Based on UAV-RGB Imagery and Mask R-CNN”) in view of Shendryk (US2023/0162441) and Li et al. (“Using multisource satellite products to estimate forest aboveground biomass in Oita prefecture: a novel approach with improved accuracy and computational efficiency”).
To claim 1, Gong teach a processor-implemented method comprising:
collecting, via an Input/Output (I/O) interface, a non-spatial and an unstructured knowledge of one or more trees of a predefined region from one or more published datasets, and literatures, wherein the unstructured knowledge includes information of geographical extent and climate of the predefined region, species, type (page 3, identification of tree species, classification of trees), age of each of the one or more trees, and one or more associated biomass allocation percentages of the one or more trees (obvious in pages 2, 11-13, study of biomass of a species);
determining, via the one or more hardware processors, a real time spatial data (pages 4, 8, determine geographic location in real-time) and one or more environmental factors of each of the one or more trees using a remote sensing data, wherein the remote sensing data comprises at least one of a high-resolution satellite data, a light detection and ranging (LiDAR) data, a high-resolution optical and Synthetic Aperture Radar (SAR) data (pages 2, 11, remote sensing data acquisition such as satellite images, LiDAR, optical and synthetic aperture radar data), and one or more weather-related indicators (obvious in pages 9, in terms of ecological and geographic distribution patterns, both prefer to grow in areas with abundant rainfall, a warm climate, and fertile soil);
hierarchically (vertical) integrating, via the one or more hardware processors, the collected non-spatial and unstructured knowledge of the one or more trees with the determined real time spatial data and the one or more environmental factors of each of the one or more trees to generate a geotagged sample tree reference data for a biomass modeling (obvious in pages 3-4, combine hyperspectral data, field surveys, and image analysis)
segmenting, via the one or more hardware processors, one or more components from each of one or more trees of the geotagged sample reference data using the high-resolution satellite data to obtain a component level segmentation of the one or more trees using a mask Region-based Convolutional Neural Networks (R-CNN) technique (page 6, Fig. 3);
training, via the one or more hardware processors, a machine learning (ML) model using the obtained component level segmentation of the one or more trees and the high-resolution optical and Synthetic Aperture Radar (SAR) data to predict presence and a location of one or more components of each of the one or more trees (pages 6-7, training for image recognition, classification and segmentation based on remote sensing);
applying, via the one or more hardware processors, the trained ML model to one or more unknown trees to generate one or more segmented components of the one or more unknown trees utilizing the high-resolution optical and SAR data of the one or more unknown trees; and
training, via the one or more hardware processors, one or more machine learning (ML) models for each of the one or more segmented components of the one or more unknown trees incorporating biomass percentages and one or more associated features from the high-resolution satellite data (pages 6-13, using trained AGB model; applying AGB ML model alike to a region with unknown tree would have been interpreted as running the AGB ML model in another remote-sensed region to perform image recognition, classification and segmentation because trees are unknown before detection and identification).
In further said obviousness, Shendryk teaching generating an above ground biomass (AGB) density prediction model (abstract), wherein collecting, via an Input/Output (I/O) interface, a non-spatial and an unstructured knowledge of one or more trees of a predefined region from one or more published datasets, and literatures, wherein the unstructured knowledge includes information of geographical extent and climate of the predefined region, species, type, age of each of the one or more trees, and one or more associated biomass allocation percentages of the one or more trees (Fig. 1; paragraphs 0004, 0027-0030, satellite imagery, LiDAR, SAR, climate; paragraphs 0044-0045, 0060, 0079, vegetation types); hierarchically (vertical) integrating, via the one or more hardware processors, the collected non-spatial and unstructured knowledge of the one or more trees with the determined spatial data and the one or more environmental factors of each of the one or more trees to generate a geotagged sample tree reference data for a biomass modeling (Figs. 3A-B; paragraphs 0039-0043).
Li teach using multisource satellite products to estimate forest aboveground biomass (page 1), collecting, via an Input/Output (I/O) interface, a non-spatial and an unstructured knowledge of one or more trees of a predefined region from one or more published datasets, and literatures, wherein the unstructured knowledge includes information of geographical extent and climate of the predefined region, species, type, age (page 12) of each of the one or more trees, and one or more associated biomass allocation percentages of the one or more trees (pages 3-6, forest register data).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teachings of Shendryk and Li into the method of Gong, in order to further details in remote sensing and forest register for improving prediction accuracy of AGB ML model.
To claims 3, 8 and 13, Gong, Shendryk and Li teach claims 1, 6 and 11.
Gong, Shendryk and Li teach wherein the component level segmentation comprises at least one of one or more segmented leaves, one or more segmented branches, a segmented trunk, and a segmented bark (Gong, pages 10-11, leave, bark, branch, trunk, wherein component level segmentation are well-known in the art for assessing a tree, which would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate for improving accuracy in biomass estimation, hence Official Notice is taken).
To claim 6, Gong, Shendryk and Li teach a system (as explained in responses to claims 1 and 3 above).
To claim 11, Gong, Shendryk and Li teach one or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors (as explained in response to claim 1 above).
Allowable Subject Matter
Claims 2, 7, 12 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZHIYU LU whose telephone number is (571)272-2837. The examiner can normally be reached Weekdays: 8:30AM - 5:00PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Stephen R Koziol can be reached at (408) 918-7630. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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ZHIYU . LU
Primary Examiner
Art Unit 2669
/ZHIYU LU/Primary Examiner, Art Unit 2665 September 23, 2026
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