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 .
In the response to this Office Action, the Examiner respectfully requests that support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line numbers in the specification and/or drawing figure(s). This will assist the Examiner in prosecuting this application.
Election/Restrictions
Applicant's election without traverse of Group I: Figure 7 in the reply filed on 04/23/2026 is acknowledged.
Claims 15-18 are however drawn to non-elected group and are therefore withdrawn from consideration.
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 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.
Claims 1-14 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication 2022/0012820 A1 to Wu (hereinafter "Wu") in view of Foreign Publication CN 111982905 A to Guo et al. (hereinafter "Guo").
Regarding Claims 1 and 19, Danzl teaches a log processing system for analyzing a log under evaluation comprising: a camera; one or more processors; one or more memory devices, the memory devices comprising computer readable code configured (Claims 1, 10, 20; Figs. 1A-5, 8; Para. 73-129, 136-152 of Danzl; images of a forest area with a spatial resolution of 10 meters or higher, such as aerial images (orthophotos) and satellite images (including optical, false-color, and hyperspectral images) may be used as input to a supplementary regression algorithm to produce a grid layer which can be combined with other layers… forest stand target attribute prediction system 300 may be configured to implement the method discussed in relation to FIG. 1A and/or FIG. 1B. The forest stand target attribute prediction system 300 may be implemented with at least one server executing at least one computer program implementing the method. The forest stand target attribute prediction system 300 may comprise an internal memory or memories that is configured to store information… apparatus 800 can also include a memory or memories 804… processor 802 may be configured by the program code which when executed performs the examples and embodiments of the operations and functionality described), when executed, to cause the one or more processors to: receive images from the camera (Figs. 1A-5, 8; Para. 73-129, 136-152 of Danzl; images of a forest area with a spatial resolution of 10 meters or higher, such as aerial images (orthophotos) and satellite images (including optical, false-color, and hyperspectral images) may be used as input to a supplementary regression algorithm to produce a grid layer which can be combined with other layers… prediction layer 302 comprises an image cache 304 that receives data from various image sources 326, for example, satellite image data, aerial image data, airborne laser scanning data etc. The training system 312 receives data from the geo data integrator 320, the forest data integrator 318 and the image cache 314. This data is used to train machine-learning based algorithms to enable prediction of at least one forest stand target attribute); estimate dimensions of the log under evaluation; and determine a log class for the log under evaluation (Figs. 1A-5, 8; Para. 73-129, 136-152 of Danzl; At 108 values of a forest stand target attribute are determined for a first set of cells of a grid layer based on the empirical measurement data. Each cell of the first set of cells of the grid layer has a specific value of the forest stand target attribute. The forest stand target attribute may refer, for example, to at least one of distribution of tree species, distribution of wood classes, distribution of log classes… Direct indicator data 200 about forest stands may refer to imaging or scanning data that is available about the forest stand, and it may comprise, for example, optical and hyperspectral satellite data and/or aerial image 206, small aperture radar data and/or satellite data 208, airborne laser scanning data 210 and forest inventory data 212… Empirical measurement data 202 about the forest stands may comprise data about tree species distribution 214 in the forest stands, data about volume/biomass 216 in the forest stands, log dimension data 218 in the forest stands, log quality data 220 and wood class data 222 (for example, sawlog, pulp wood, energy wood) in the forest stands… The empirical measurement data may be used to include all trees of one or more categories (for example, pulp wood and/or saw logs) of a harvested forest stand, to ensure that the empirical measurement data is representative for the entire forest stand… airborne laser scanning 210 enables determination of the biomass volume 216 and log dimension 218).
Danzl does not explicitly disclose identifying the log under evaluation within the images.
However, Guo teaches identifying a log under evaluation within images (Abstract; Fig. 1; Para. 9-42 of Guo; image acquisition module includes a high-definition camera for image acquisition on the surface of each wood to be detected. The high-definition camera collects the surface image of each wood to be detected, and sends the collected surface image of each wood to the image processing module. Sequentially number the collected surface images of each wood, the numbers are 1, 2,...,i,...,n, and send the collected surface image numbers of each wood to the storage database… image processing module is used to receive the surface image of each wood sent by the image acquisition module, perform feature extraction on the received surface image of each wood, extract the color and texture organization in the surface image of each wood, and extract the data stored in the storage database The standard color and standard texture organization in the surface image of various types of wood, the color and texture organization in the surface image of each wood are compared with the corresponding standard color and standard texture organization in the surface image of various types of wood, and the statistics are calculated).
Therefore, at the time when the invention was filed, it would have been obvious to a person of ordinary skill in the art to include identifying the log under evaluation within the images using the teachings of Guo in order to modify the system and method taught by Danzl. The motivation to combine these analogous arts would have been to provide a wood quality intelligent detection system based on industrial big data image analysis (Abstract of Guo).
Regarding Claim 2, the combination of Danzl and Guo teaches that the computer readable code is configured, when executed, to cause the one or more processors to utilize one or more machine learning techniques (Claim 10; Figs. 1A-5; Para. 71-129 of Danzl; machine learning algorithms used for this purpose are preferably regression algorithms such as an error-minimizing, non-linear machine learning algorithm, such as an Artificial Neural Network, Decision Tree, Random Forest, or Gradient Boosted Trees)
Regarding Claim 3, the combination of Danzl and Guo teaches that the computer readable code is configured, when executed, to cause the one or more processors to utilize one or more machine learning techniques to perform at least one of: identifying the log under evaluation within the images; estimating the dimensions of the log under evaluation; determining the log class for the log under evaluation; detecting and classifying a variation within the log under evaluation; detecting and classifying an object within the images using multi-object tracking and segmentation; and tracking of a weight for a log (Figs. 2-5, 8; Para. 74-129, 136-152 of Danzl; Empirical measurement data 202 about the forest stands may comprise data about tree species distribution 214 in the forest stands, data about volume/biomass 216 in the forest stands, log dimension data 218 in the forest stands, log quality data 220 and wood class data 222 (for example, sawlog, pulp wood, energy wood) in the forest stands).
Regarding Claim 4, the combination of Danzl and Guo teaches that the dimensions of the log under evaluation are determined using photogrammetry (Figs. 2-5, 8; Para. 74-129, 136-152 of Danzl; images of a forest area with a spatial resolution of 10 meters or higher, such as aerial images (orthophotos) and satellite images (including optical, false-color, and hyperspectral images) may be used as input to a supplementary regression algorithm to produce a grid layer which can be combined with other layers).
Regarding Claim 5, the combination of Danzl and Guo teaches that the dimensions of the log under evaluation are determined with a dimensional precision of about 0.5 inches or less at a visual depth of about 30 feet from the camera (Figs. 2-5, 8; Para. 74-129, 136-152 of Danzl; images of a forest area with a spatial resolution of 10 meters or higher, such as aerial images (orthophotos) and satellite images (including optical, false-color, and hyperspectral images) may be used as input to a supplementary regression algorithm to produce a grid layer which can be combined with other layers… Designing a system wherein the dimensions of the log under evaluation are determined with a dimensional precision of about 0.5 inches or less at a visual depth of about 30 feet from the camera would only require routine skill for a person of ordinary skill in the art based on the combined teachings of Danzl and Guo. Therefore, one of ordinary skill in the art would have pursued having the dimensions of the log under evaluation are determined with a dimensional precision of about 0.5 inches or less at a visual depth of about 30 feet from the camera as this merely involves choosing from a finite number of identified, predictable solutions with a reasonable expectation of success that would have yielded predictable results and can be accomplished without any undue experimentation in order to provide a wood quality intelligent detection system based on industrial big data image analysis. In addition, it has been held that where the general conditions of a claim are disclosed in the prior art, discovering the optimum or workable ranges involves only routine skill in the art. In re Aller, 105 USPQ 233).
Regarding Claim 6, the combination of Danzl and Guo teaches that the dimensions of the log are detected within less than about one second of initial observation by the camera (Abstract; Fig. 1; Para. 9-42 of Guo; image acquisition module includes a high-definition camera for image acquisition on the surface of each wood to be detected. The high-definition camera collects the surface image of each wood to be detected, and sends the collected surface image of each wood to the image processing module… Designing a system wherein the dimensions of the log are detected within less than about one second of initial observation by the camera would only require routine skill for a person of ordinary skill in the art based on the combined teachings of Danzl and Guo. Therefore, one of ordinary skill in the art would have pursued having the dimensions of the log are detected within less than about one second of initial observation by the camera as this merely involves choosing from a finite number of identified, predictable solutions with a reasonable expectation of success that would have yielded predictable results and can be accomplished without any undue experimentation in order to provide a wood quality intelligent detection system based on industrial big data image analysis. In addition, it has been held that where the general conditions of a claim are disclosed in the prior art, discovering the optimum or workable ranges involves only routine skill in the art. In re Aller, 105 USPQ 233).
Regarding Claim 7, the combination of Danzl and Guo teaches that the computer readable code is configured, when executed, to cause the one or more processors to sort the log under evaluation based on the log class (Figs. 2-5, 8; Para. 74-129, 136-152 of Danzl; Empirical measurement data 202 about the forest stands may comprise data about tree species distribution 214 in the forest stands, data about volume/biomass 216 in the forest stands, log dimension data 218 in the forest stands, log quality data 220 and wood class data 222 (for example, sawlog, pulp wood, energy wood) in the forest stands… Abstract; Fig. 1; Para. 32-42 of Guo; the weight analysis module is used for receiving the total volume of each wood sent by the wood grain area detection module and simultaneously receiving the weight set of each wood sent by the weight detection module to form a total volume set V (V) of each wood1,V2,...,Vi,...,Vn)，Vi… Expressing the total volume of the ith wood, extracting the standard weight of the oven dry wood with various unit volumes stored in the storage database, comparing the weight of each wood with the standard weight of the oven dry wood with the corresponding volume, and counting the weight comparison difference value of each wood, thereby avoiding the water loss problem after people use wet wood, preventing the wood from cracking and warping, and forming a weight comparison difference value set delta G of each woodn(Δg1,Δg2,...,Δgi,...,Δgn)，Δgi… Expressing the weight of the ith wood material compared with the difference value… The quantity comparison difference value is sent to an analysis server… the analysis server is used for receiving the weight comparison difference value of each wood sent by the weight analysis module, receiving the decay grade of each wood sent by the gas analysis module, extracting the decay influence coefficient corresponding to each decay grade stored in the storage database, and calculating the comprehensive quality influence coefficient of each wood).).
Regarding Claim 8, the combination of Danzl and Guo teaches that the computer readable code is configured, when executed, to cause the one or more processors to detect one or more variations in the log under evaluation (Figs. 2-5, 8; Para. 74-129, 136-152 of Danzl; Empirical measurement data 202 about the forest stands may comprise data about tree species distribution 214 in the forest stands, data about volume/biomass 216 in the forest stands, log dimension data 218 in the forest stands, log quality data 220 and wood class data 222 (for example, sawlog, pulp wood, energy wood) in the forest stands).
Regarding Claim 9, the combination of Danzl and Guo teaches that the one or more variations include a feature that deviates from a conical frustrum shape, a defect, a surface variation, a crook, a sweep, or a fork (Abstract; Fig. 1; Para. 32-42 of Guo; the weight analysis module is used for receiving the total volume of each wood sent by the wood grain area detection module and simultaneously receiving the weight set of each wood sent by the weight detection module to form a total volume set V (V) of each wood1,V2,...,Vi,...,Vn)，Vi… Expressing the total volume of the ith wood, extracting the standard weight of the oven dry wood with various unit volumes stored in the storage database, comparing the weight of each wood with the standard weight of the oven dry wood with the corresponding volume, and counting the weight comparison difference value of each wood, thereby avoiding the water loss problem after people use wet wood, preventing the wood from cracking and warping, and forming a weight comparison difference value set delta G of each woodn(Δg1,Δg2,...,Δgi,...,Δgn)，Δgi… Expressing the weight of the ith wood material compared with the difference value… The quantity comparison difference value is sent to an analysis server… the analysis server is used for receiving the weight comparison difference value of each wood sent by the weight analysis module, receiving the decay grade of each wood sent by the gas analysis module, extracting the decay influence coefficient corresponding to each decay grade stored in the storage database, and calculating the comprehensive quality influence coefficient of each wood).
Regarding Claim 10, the combination of Danzl and Guo teaches that the computer readable code is configured, when executed, to cause the one or more processors to detect the log under evaluation within a work area using multi-object tracking and segmentation (Figs. 2-5, 8; Para. 74-129, 136-152 of Danzl; Direct indicator data 200 about forest stands may refer to imaging or scanning data that is available about the forest stand, and it may comprise, for example, optical and hyperspectral satellite data and/or aerial image 206, small aperture radar data and/or satellite data 208, airborne laser scanning data 210 and forest inventory data 212… Empirical measurement data 202 about the forest stands may comprise data about tree species distribution 214 in the forest stands, data about volume/biomass 216 in the forest stands, log dimension data 218 in the forest stands, log quality data 220 and wood class data 222 (for example, sawlog, pulp wood, energy wood) in the forest stands).
Regarding Claim 11, the combination of Danzl and Guo teaches that the computer readable code is configured, when executed, to cause the one or more processors to detect another moving object within the work area using multi-object tracking and segmentation (Figs. 2-5, 8; Para. 74-129, 136-152 of Danzl; Direct indicator data 200 about forest stands may refer to imaging or scanning data that is available about the forest stand, and it may comprise, for example, optical and hyperspectral satellite data and/or aerial image 206, small aperture radar data and/or satellite data 208, airborne laser scanning data 210 and forest inventory data 212… Empirical measurement data 202 about the forest stands may comprise data about tree species distribution 214 in the forest stands, data about volume/biomass 216 in the forest stands, log dimension data 218 in the forest stands, log quality data 220 and wood class data 222 (for example, sawlog, pulp wood, energy wood) in the forest stands… The empirical measurement data may be used to include all trees of one or more categories (for example, pulp wood and/or saw logs) of a harvested forest stand, to ensure that the empirical measurement data is representative for the entire forest stand).
Regarding Claim 12, the combination of Danzl and Guo teaches that the computer readable code is configured, when executed, to cause the one or more processors to estimate a weight of each log, wherein the determination of the log class for the log under evaluation is dependent upon the weight that is estimated (Abstract; Fig. 1; Para. 32-42 of Guo; the weight analysis module is used for receiving the total volume of each wood sent by the wood grain area detection module and simultaneously receiving the weight set of each wood sent by the weight detection module to form a total volume set V (V) of each wood1,V2,...,Vi,...,Vn)，Vi… Expressing the total volume of the ith wood, extracting the standard weight of the oven dry wood with various unit volumes stored in the storage database, comparing the weight of each wood with the standard weight of the oven dry wood with the corresponding volume, and counting the weight comparison difference value of each wood, thereby avoiding the water loss problem after people use wet wood, preventing the wood from cracking and warping, and forming a weight comparison difference value set delta G of each woodn(Δg1,Δg2,...,Δgi,...,Δgn)，Δgi… Expressing the weight of the ith wood material compared with the difference value… The quantity comparison difference value is sent to an analysis server… the analysis server is used for receiving the weight comparison difference value of each wood sent by the weight analysis module, receiving the decay grade of each wood sent by the gas analysis module, extracting the decay influence coefficient corresponding to each decay grade stored in the storage database, and calculating the comprehensive quality influence coefficient of each wood).
Regarding Claim 13, the combination of Danzl and Guo teaches that computer readable code is configured, when executed, to cause the one or more processors to use Bayesian updating to determine the log class for the log under evaluation (Figs. 2-5, 8; Para. 74-129, 136-152 of Danzl; a Bayesian hyperparameter optimization approach may be used).
Regarding Claim 14, the combination of Danzl and Guo teaches that the computer readable code is configured, when executed, to cause the one or more processors to emphasize a representation of the log under evaluation within a display image and to cause the display image to be presented on a display, and wherein the computer readable code is configured, when executed, to cause the one or more processors to cause detailed information regarding the log under evaluation to be presented in the display (Abstract; Fig. 1; Para. 32-42 of Guo; The display terminal is used to receive the strength grade of each wood and each wood number of unqualified quality sent by the analysis server, and display it).
Regarding Claim 20, the combination of Danzl and Guo teaches detecting the log under evaluation within a work area using multi-object tracking and segmentation; detecting one or more variations in the log under evaluation (Figs. 2-5, 8; Para. 74-129, 136-152 of Danzl; Empirical measurement data 202 about the forest stands may comprise data about tree species distribution 214 in the forest stands, data about volume/biomass 216 in the forest stands, log dimension data 218 in the forest stands, log quality data 220 and wood class data 222 (for example, sawlog, pulp wood, energy wood) in the forest stands); estimating a weight of each log; and causing the log under evaluation to be sorted based on the log class, wherein the determination of the log class for the log under evaluation is dependent upon the weight that is estimated, the type of variations in the log under evaluation, the number or size of variations in the log under evaluation (Abstract; Fig. 1; Para. 32-42 of Guo; the weight analysis module is used for receiving the total volume of each wood sent by the wood grain area detection module and simultaneously receiving the weight set of each wood sent by the weight detection module to form a total volume set V (V) of each wood1,V2,...,Vi,...,Vn)，Vi… Expressing the total volume of the ith wood, extracting the standard weight of the oven dry wood with various unit volumes stored in the storage database, comparing the weight of each wood with the standard weight of the oven dry wood with the corresponding volume, and counting the weight comparison difference value of each wood, thereby avoiding the water loss problem after people use wet wood, preventing the wood from cracking and warping, and forming a weight comparison difference value set delta G of each woodn(Δg1,Δg2,...,Δgi,...,Δgn)，Δgi… Expressing the weight of the ith wood material compared with the difference value… The quantity comparison difference value is sent to an analysis server… the analysis server is used for receiving the weight comparison difference value of each wood sent by the weight analysis module, receiving the decay grade of each wood sent by the gas analysis module, extracting the decay influence coefficient corresponding to each decay grade stored in the storage database, and calculating the comprehensive quality influence coefficient of each wood).
Conclusion
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/ABHISHEK SARMA/
Primary Examiner, Art Unit 2621
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