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 § 102 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 – (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. (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. Claim(s) 1-2, 5, 7-8, 10, 13, 15, 17, 20 is/are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Xu ( US 20220237891 A1 ). Regarding claim 1 , Xi discloses a method, comprising: obtaining a first petrographic image (Fig. 3, [0045] obtain core photographs as digital images) ; determining, by a computer processor (Fig. 10, [0075] a computer system including one or more processors) , a first plurality of region proposals based on the first petrographic image and a selective searching function (Fig. 3-4, [0046]-[0047] extracting sliding window samples of a core data image) , wherein a respective region proposal among the first plurality of region proposals corresponds to a plurality of pixels in the first petrographic image according to at least one predetermined dimension (Fig. 4, [0047] wherein the sliding window region has a specific width and length, each corresponding to a number of pixels; [0004] the sliding window size is a predetermined window size) ; determining, by the computer processor, color histogram data for the first petrographic image (Fig. 4, [0047]; Fig. 3, [0046] quantitative image attributes may include color data in particular pixel or a moving window, quantitative image attributes may be recorded as an array of histogram distributions) ; determining, by the computer processor, input image data based on the first petrographic image, the first plurality of region proposals, and the color histogram data (Fig. 4, [0047] determining statistical parameters (i.e. input image data) based on the histogram and feature data of the sliding window regions of the core data image, wherein the statistical parameters (445) are used as the final quantitative image attributes for input to a predictive data processing operation) ; and determining, by the computer processor, one or more rock objects using the input image data and a first machine-learning model (Fig. 3, Fig. 8, [0066] predicted rock data are determined for a geological region of interest using various quantitative image attributes and a machine-learning algorithm) . Regarding claim 2 , Xu discloses the method of claim 1 as applied above. Xu further discloses determining, using the one or more rock objects, a first classified image based on the first petrographic image ([0055] a reservoir properties estimator may visualize and export various classification and regression results of predicted rock types and predicted rock properties) ; determining a training dataset based on a plurality of classified images and the first classified image; obtaining a second machine-learning model; and training the second machine-learning model using the training dataset, a plurality of machine-learning epochs, and a supervised learning algorithm ([0040] the reservoir properties estimator may store well logs and data regarding core samples, and further analyze the well log data, the core sample data, seismic data, and/or other types of data to generate and/or update one or more machine-learning models; [0040] wherein the machine-learning models may be supervised learning models and the reservoir properties estimator may generate a large amount of data for training a model; [0038] multiple models may be trained and combined (i.e. plurality of ML epochs)) . Regarding claim 5 , Xu discloses the method of claim 1 as applied above. Xu further discloses wherein the selective search function determines a respective region proposal among the first plurality of region proposals for an image object of interest within a respective petrographic image, and wherein the respective region proposal corresponds to a set of pixels that form a sub-image (Fig. 4, [0046]-[0047] a selected sliding window image including quantitative image attributes from among the sliding window data is shown which is comprised of pixels) . Regarding claim 7 , Xu discloses the method of claim 1 as applied above. Xu further discloses obtaining a plurality of petrographic images (Fig. 3, [0045] obtain core photographs as digital images) ; determining a plurality of rock objects from the plurality of petrographic images using the first machine-learning model; determining one or more image clusters using the plurality of rock objects and a clustering function (Fig. 3, Fig. 7, [0052] various rock types are clustered by an ML algorithm and a rock type cluster is formed that corresponds to a particular geological region of interest; [0054] the visual rock types may be indexed by depth in the output data (i.e. determining image clusters)) ; and determining a training dataset based on the one or more image clusters ([0055] the reservoir properties estimator inputs the data output by the clustering operations; [0040] the reservoir properties estimator may store well logs and data regarding core samples, and further analyze the well log data, the core sample data, seismic data, and/or other types of data to generate and/or update one or more machine-learning models; [0040] wherein reservoir properties estimator may generate a large amount of data for training a model) . Regarding claim 8 , Xu discloses the method of claim 7 as applied above. Xu further discloses wherein the clustering function is an unsupervised machine-learning algorithm ([0054] a cluster operation is performed based on an unsupervised classification using one or more machine-learning algorithms) . Regarding claim 10 , Xu discloses the method of claim 1 as applied above. Xu further discloses wherein the first machine-learning model is an artificial neural network comprising an input layer, a plurality of hidden layers, and an output layer ([0007] the machine-learning model is a neural network model that obtains image data as inputs to an image layer; [0041] a neural network may include one or more hidden layers; [0067] the neural network model may output at the output layer) . Regarding claim 13 , Xu discloses everything claimed as applied above (see rejection of claim 1) and further including a system, comprising: an image acquisition system comprising a camera device; and a reservoir simulator coupled to the image acquisition system, wherein the reservoir simulator comprises a computer processor (Fig. 10, [0075] computer system including processors; Fig. 1, [0022] reservoir properties estimator including a computer system as in Fig. 10 for processing core data; [0059] core data photo may be captured by a camera) . Regarding claim 15 , Xu discloses the system of claim 13 as applied above. Xu further discloses everything claimed as applied above (See rejection of claim 2). Regarding claim 17 , Xu discloses the system of claim 13 as applied above. Xu further discloses everything claimed as applied above (See rejection of claim 5). Regarding claim 20 , Xu discloses the method of claim 13 as applied above. Xu further discloses the system of claim 13 as applied above. Xu further discloses a drilling system (Fig. 1, [0021] drilling system) ; and a control system coupled to the drilling system and the reservoir simulator (Fig. 1, [0021] control system coupled to the drilling system and reservoir properties estimator) , wherein the control system is configured to transmit a command to the drilling system to perform a drilling operation based on predicted data for a geological region of interest ([0042] a control system may communicate geosteering commands to the drilling system, the commands based on updates from the reservoir properties estimator; [0056] data from an ML model is used to update a geological model of a region of interest and to determine a well path for a geosteering operation) , and wherein the predicted data is determined using a trained model that is trained using a training dataset comprising a classified petrographic image corresponding to the first petrographic image and the one or more rock objects (Fig. 3, Fig. 7, [0052] various rock types are clustered by an ML algorithm and a rock type cluster is formed that corresponds to a particular geological region of interest; [0054] the visual rock types may be indexed by depth in the output data (i.e. determining image clusters); [0040] the reservoir properties estimator may store well logs and data regarding core samples, and further analyze the well log data, the core sample data, seismic data, and/or other types of data to generate and/or update one or more machine-learning models; [0040] wherein reservoir properties estimator may generate a large amount of data for training a model) . 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. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness . Claim (s) 4, 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu ( US 20220237891 A1 ) in view of Bozkir ( Bozkir , A. S., et al. "Geological strength index prediction by vision and machine learning methods." ISRM EUROCK . ISRM, 2020. ) . Regarding claim 4 , Xu discloses the method of claim 1 as applied above. Xu further discloses wherein the first machine-learning model is a support vector machine ([0038] wherein the machine learning model maybe be a support vector machine) . Xu fails to disclose wherein the color histogram data comprises a plurality of histogram oriented gradients, and wherein the support vector machine uses the plurality of histogram oriented gradients and a kernel function to determine the one or more rock objects. Bozkir , in a related system from the same field of endeavor of computer vision and machine learning methods for geological applications (Abstract), discloses wherein histogram data comprises a plurality of histogram oriented gradients (Introduction third paragraph: histogram of oriented gradients HOG; section 3.1.2 first paragraph: HOG data to represent portions of an image) , and wherein the support vector machine uses the plurality of histogram oriented gradients and a kernel function to determine the one or more rock objects (section 3.2 second paragraph: support vector machine can be trained for linear and nonlinear datasets via linear and radial basis kernels respectively; Fig. 3, Introduction third paragraph: histogram of oriented gradients HOG and ML method such as support vector machine SVM can be used to determine geological strength index values of rock outcrops from images) . It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to combine Bozkir and Xu wherein the histogram data comprises a plurality of histogram oriented gradients and wherein the support vector machine uses the plurality of histogram oriented gradients and a kernel function to determine the one or more rock objects, as disclosed by Bozkir , as part of a method for determining rock objects in petrographic image data, as disclosed by Xu, for the purpose of determining discriminative visual cues located in geological photos (See Bozkir : section 3.1, first paragraph). Regarding claim 16 , Xu discloses the system of claim 13 as applied above . Xu in view of Bozkir further discloses everything claimed as applied above (see rejection of claim 4). Claim (s) 11, 14, is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu ( US 20220237891 A1 ) in view of Alquattan ( WO 2022011394 A1 ) . Regarding claim 11 , Xu discloses the method of claim 1 as applied above. Xu fails to disclose wherein the first petrographic image is acquired using a petrological microscope. Alquattan , in a related system from the same field of endeavor of analyzing petrographic images ([0001]), discloses wherein the first petrographic image is acquired using a petrological microscope (Fig. 1, [0022] petrographic samples are analyzed using a microscope system which outputs petrographic sample images) . It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to combine Alquattan with Xu wherein the first petrographic image is acquired using a petrological microscope, as disclosed Alquattan , as part of a method for determining rock objects in petrographic image data, as disclosed by Xu, for the purpose of efficient use of computational resources for performing automated analysis of petrographical sample images (see Alquattan : [0010]). Regarding claim 14 , Xu discloses the system of claim 13 as applied above. Xu in view of Alquattan further discloses everything claimed as applied above (see rejection of claim 11). Allowable Subject Matter Claim s 3, 6, 9, 12, 18-19 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. The following is a statement of reasons for the indication of allowable subject matter: Regarding claim 3 , Xu discloses the method of claim 1 as applied above. Xu further discloses obtaining a plurality of petrographic images (Fig. 3, [0045] obtain core photographs as digital images) . However, Xu fails to disclose determining a second plurality of region proposals based on the plurality of petrographic images and the selective searching function; obtaining training data comprising a plurality of classified images based on the plurality of petrographic images and the second plurality of region proposals; and performing a training operation on a second machine-learning model using the training data, wherein the second machine-learning model determines one or more predicted rock labels for a respective input petrographic image, and wherein second machine-learning model is updated iteratively until the one or more predicted rock labels satisfy a predetermined criterion. Similar reasoning applies to claim 19 . Regarding claim 6 , Xu discloses the method of claim 1 as applied above. However, Xu fails to disclose wherein the selective search function is a hierarchical process based on one or more similarity measures selected from a group consisting of a color metric, a texture metric, a size metric, and a shape metric. Regarding claim 9 , Xu discloses the method of claim 1 as applied above. However, Xu fails to disclos e performing an edge smoothing operation on the first petrographic image to produce an adjusted petrographic image, and wherein the color histogram data is determined using the adjusted petrographic image. Regarding claim 12 , Xu discloses the method of claim 1 as applied above. Xu further discloses determining a presence of hydrocarbon deposits using the predicted data ([0056] the predicted data is used in determining the presence of hydrocarbon deposits within a geological region) . However, Xu fails to disclos e determining predicted data for a geological region of interest using one or more petrographic images and a second machine-learning model, wherein the second machine-learning model is trained using a training dataset comprising the first petrographic image and the one or more rock objects . Similar reasoning applies to claim 18 . Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Tariq ( WO 2019246250 A1 ) discloses training a machine learning model based on selected subset of an image and comparing to a predetermined threshold . Heo ( US 20150332471 A1 ) discloses detecting image feature including generating a histogram based on color data and extracting image proposals. Andersen ( US 20170032532 A1 ) discloses analysis of digital core image including generating multiple segmented images and determining rock properties for planning wellbore operations . 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