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 .
DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
1. This action is responsive to communications: Application filed on 4/5/2024, and Drawings filed on 4/5/2024.
2. Claims 1–20 are pending in this case. Claim 1, 11 are independent claims.
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.
Claim Rejections - 35 U.S.C. § 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 1-8, 11-8 rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
As to claim 1:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process (method)
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “generating a lithology vector associated with each image of the plurality of images to form an image/vector set comprising a plurality of image/vector pairs, the lithology vector comprising a plurality of rock types and a percentage of each of the plurality of rock types identified in a respective image;” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “balancing the image/vector set based on an occurrence of the plurality of rock types across the plurality of image/vector pairs to generate the balanced training set.” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “receiving a plurality of images of cuttings from a geological formation;” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “receiving a plurality of images of cuttings from a geological formation;” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g). (“The collecting step is recited at a high level of generality (i.e., as a general means of gathering network traffic data for use in the comparison step), and amounts to mere data gathering, which is a form of insignificant extra-solution activity.”).
As to claim 2:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process (method)
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “wherein the balancing is configured to account for underrepresented rock types in one or more of the image/vector pairs.” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
The analysis of the parent claim is incorporated.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
The analysis of the parent claim is incorporated.
v1.2 7/9/2024
As to claim 3:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process (method)
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “wherein the balancing comprises: determining a vector sum across the plurality of lithology vector pairs for each of the plurality of rock types and a lower sum threshold value; in response to determining that a respective vector sum is less than the lower sum threshold value, excluding from the image/vector set, all rock types having a vector sum less than the lower sum threshold value to generate a modified image/vector set; determining a repetition vector based on the modified image/vector set and the plurality of rock types; and repeating and storing each image/vector pair in the image/vector set according to the repetition vector.” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
The analysis of the parent claim is incorporated.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
The analysis of the parent claim is incorporated.
As to claim 4:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process (method)
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “wherein the excluding comprises one of: flagging each rock type associated with the respective vector sum as unknown; or removing all image/vector pairs having a vector sum less than the lower sum threshold value.” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
The analysis of the parent claim is incorporated.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
The analysis of the parent claim is incorporated.
As to claim 5:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process (method)
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “ wherein determining the repetition vector comprises: determining a minimization factor corresponding to a most nearly equal representation of rock types; generating a matrix having each lithology vector as a row; determining a mean value of each lithology vector; and rounding off each percentage based on the mean value and the minimization factor.” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
The analysis of the parent claim is incorporated.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
The analysis of the parent claim is incorporated.
As to claim 6:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process (method)
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “wherein rock types flagged as unknown are summed together within the image/vector set.” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
The analysis of the parent claim is incorporated.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
The analysis of the parent claim is incorporated.
As to claim 7:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process (method)
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “wherein each of the images is linked to a depth in the geological formation at which a respective cutting was obtained.” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “wherein each of the images is linked to a depth in the geological formation at which a respective cutting was obtained.” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g). (“The collecting step is recited at a high level of generality (i.e., as a general means of gathering network traffic data for use in the comparison step), and amounts to mere data gathering, which is a form of insignificant extra-solution activity.”).
As to claim 8:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process (method)
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “wherein the sum of each lithology vector equals 1.” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
The analysis of the parent claim is incorporated.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
The analysis of the parent claim is incorporated.
Claims 11-18 are the media claims and rejected for the same reason as claims 1-8.
Claims 2-6, 12-16 would be allowable if rewritten or amended to overcome the 101rejection(s)
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, 8, 9, 10, 11, 18, 19, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu, CN 111563445 A, in view of Vaidyanathan, US11315228B2.
With regard to claim 1:
Xu discloses a method for training a machine learning engine from a balanced training set, the method comprising: receiving a plurality of images of cuttings from a geological formation (see fig. 8 for images of cutting from a geological formation: “the classification information comprises a plurality of rock category fraction result corresponding to the sample set corresponding to the rock category, taking a plurality of rock category fraction result in one of the classification result of the rock sheet micro-image to be identified. Referring to FIG. 9, for example to obtain 8 fraction result, each fraction result corresponding to a certain category of rock, selecting the highest score is the classification result of the microscopic image of the rock sheet to be identified. compared with the field directly photographing rock or using geological hammer to knock a rock sample, and the macroscopic rock picture, the image if using the convolutional neural network after image training to identify, only can understand lithology and primarily know the rock mineral component, The invention adopts the rock grinding into sheet, using the optical property of mineral in rock under the microscope to observe and using professional microscope camera imaging, the structure and structure of the component rock grain of the mineral composition is detailed. In this way, the under-mirror rock sheet mainly uses the optical characteristics of the mineral; the difference is small under the environment influence, the optical characteristics of each microscope are consistent and the optical characteristics of rock mineral (macroscopical photo; the weather of the marty-rainy day and rainy day, the camera is influenced by the natural light condition, and the macroscopical photo is easy to have many noise points and useless information such as background environment and so on, which is not rock part), the performance is stable, and the scale of the photo is unified.” ); generating a lithology vector associated with each image of the plurality of images to form an image/vector set comprising a plurality of image/vector pairs, the lithology vector comprising a plurality of rock types and a percentage of each of the plurality of rock types identified in a respective image (see table 7 with vector pairing of the vectors that represent rock type percentages and images from 8(a) to 8(l) as shown in fig. 8, where the rocks include volcanic rock, carbonate rock and clastic rock: “The weight table given by the computer can be seen, all the pictures are the same type single-polarized orthogonal polarization discrimination error of rock. The computer is more accurate to identify the morphological characteristics of the carbonate rock. Referring to FIG. 8, a picture (a-f) is an orthogonal polarization picture, wherein b, c, d, e, are more than 90 % weights are classified as a single-polarized picture, picture (g_l) is a single-polarized picture, wherein g, h, i, j, l are 80 % the left and right weights are classified as orthogonal polarized picture. The reason is that the picture collects actual working rock sheets, some of the sheets are subjected to dyeing treatment, the calcite is dyed into red, and the dolomite is not dyed red. the collected photo has calcite dyeing visual field; the calcite has no dyeing visual field; the visual field with dolomite. the remaining test set photograph is not dyed grey rock and is no classification error, so the computer judges the orthogonal polarization and single-polarized photograph, the dyeing process of carbonate rock for judging the single orthogonal polarization classification has great influence, but for the type of positive error judgment has no obvious change. The simple color change does not affect the influence of the judgment of carbonate rock shape. (a-f) is carbonate rock orthogonal polarization photo (g_l) is carbonate rock single polarization photo. In summary, the present invention establishes an identification model of the underlying sheet rock image based on a residual neural network (ResNet) in a convolutional neural network. For metamorphic rock, volcanic rock, sedimentary rock and carbonate rock four rock types for effective identification, four types of rock identification total correct rate of the test set reaches 98.8 %, wherein the bad rock identification correct rate 100 %, volcanic identification correct rate is 97.6 %, clastic rock identification accuracy rate is 96.3 %, carbonate rock identification correct rate 100 % .”).
Xu does not disclose the aspect of balancing the image/vector set based on an occurrence of the plurality of rock types across the plurality of image/vector pairs to generate the balanced training set.
However Vaidyanathan discloses the aspect of balancing the image/vector set based on an occurrence of the plurality of rock types across the plurality of image/vector pairs to generate the balanced training set (see fig. 5 for categories which include rock type such as Fe, Si, Al Mn, Ca, paragraph 46 and 47: “Referring now to FIG. 6, for purposes of reference, an array of geological identifiers 600 (i.e., physical properties) that may be used by the image classification model to identify mineral composition is presented. A first identifier category (“Origin”) 610 refers to whether a mineral is Igneous, Sedimentary, or Metamorphic, and a second identifier category (“Grain Size”) 612 refers to whether the mineral is fine, coarse, rounded, jagged, banded, or non-banded. In addition, a third identifier category (“Specific Gravity”) 614 refers to a ratio of a particular mineral's mass to that of mass of an equal volume of water and a fourth identifier category (“Magnetism”) 616 refers to the ability of the rock to attract metallic objects. A fifth identifier category (“Fracture/Cleavage”) 618 describes the way a mineral breaks, and a sixth identifier category (“Crystal Habit”) 620 refers to the commonly found shape or form of the mineral, such as Prismatic, Tabular, Acicular, or Fibrous. A seventh identifier category (“Streak”) 622 refers to the color left behind when the mineral is finely crushed, while an eighth identifier category (“Color”) 624 refers to the color of the mineral, such as reddish-brown (Hematite), green (Olivine), violet (Amethyst), etc. In addition, a ninth identifier category (“Hardness”) 626 is measured on the 10-point Mohs scale, a tenth identifier category (“Luster”) 628 refers to whether a mineral is Metallic, Submetallic, Adamantine, Glassy, Resinous, Waxy, or Pearly, and an eleventh identifier category (“Structure and Texture”) 630 refers to the size, shape, and arrangement of the grains or crystals, as well as the extent of homogeneity (i.e., uniformity of composition throughout). The descriptor concepts identified for each category above should be understood to serve primarily of examples, and non-limiting. For example, concept names or labels may be customized for the particular mining operation, or include additional labels in some cases and fewer in others. One or more of these identifier categories can be fed into the image classification model as a basis by which the model may determine an identity of the mineral(s) present in a captured image. While a wide range of physical properties may be utilized by the system as identifier categories (see FIG. 6), it may be appreciated that the image classification model may offer greater accuracy when relying on physical properties that are more readily discernable through images. In other words, in seeking to remotely identify minerals, in order to classify certain physical properties of the mineral, the collection of physical or tangible samples may be required for testing for physical properties of the samples. For example, while some physical properties might be better detected based on XRF information or other laboratory-type analysis (e.g., such as origin or specific gravity or magnetism), the image classification model can provide a more reliable and accurate assessment when the primary identifiers selected take into account the type of data (imagery) which is being processed.”) It would have been obvious to one of ordinary skill in the art, at the time the filing was made to apply Vaidyanathan to Xu so the image vector set is balanced that would be better suited for training purposes to get better training result.
With regard to claims 8 and 19:
Xu and Vaidyanathan disclose the method according to claim 1, wherein the sum of each lithology vector equals 1 (Xu see table 7 wherein for each image the sum of each vector add up to 100%).
With regard to claims 9 and 18:
Xu and Vaidyanathan disclose the method according to claim 1, further comprising, following the balancing (see fig. 5 for categories which include rock type such as Fe, Si, Al Mn, Ca, paragraph 46 and 47: “Referring now to FIG. 6, for purposes of reference, an array of geological identifiers 600 (i.e., physical properties) that may be used by the image classification model to identify mineral composition is presented. A first identifier category (“Origin”) 610 refers to whether a mineral is Igneous, Sedimentary, or Metamorphic, and a second identifier category (“Grain Size”) 612 refers to whether the mineral is fine, coarse, rounded, jagged, banded, or non-banded. In addition, a third identifier category (“Specific Gravity”) 614 refers to a ratio of a particular mineral's mass to that of mass of an equal volume of water and a fourth identifier category (“Magnetism”) 616 refers to the ability of the rock to attract metallic objects. A fifth identifier category (“Fracture/Cleavage”) 618 describes the way a mineral breaks, and a sixth identifier category (“Crystal Habit”) 620 refers to the commonly found shape or form of the mineral, such as Prismatic, Tabular, Acicular, or Fibrous. A seventh identifier category (“Streak”) 622 refers to the color left behind when the mineral is finely crushed, while an eighth identifier category (“Color”) 624 refers to the color of the mineral, such as reddish-brown (Hematite), green (Olivine), violet (Amethyst), etc. In addition, a ninth identifier category (“Hardness”) 626 is measured on the 10-point Mohs scale, a tenth identifier category (“Luster”) 628 refers to whether a mineral is Metallic, Submetallic, Adamantine, Glassy, Resinous, Waxy, or Pearly, and an eleventh identifier category (“Structure and Texture”) 630 refers to the size, shape, and arrangement of the grains or crystals, as well as the extent of homogeneity (i.e., uniformity of composition throughout). The descriptor concepts identified for each category above should be understood to serve primarily of examples, and non-limiting. For example, concept names or labels may be customized for the particular mining operation, or include additional labels in some cases and fewer in others. One or more of these identifier categories can be fed into the image classification model as a basis by which the model may determine an identity of the mineral(s) present in a captured image. While a wide range of physical properties may be utilized by the system as identifier categories (see FIG. 6), it may be appreciated that the image classification model may offer greater accuracy when relying on physical properties that are more readily discernable through images. In other words, in seeking to remotely identify minerals, in order to classify certain physical properties of the mineral, the collection of physical or tangible samples may be required for testing for physical properties of the samples. For example, while some physical properties might be better detected based on XRF information or other laboratory-type analysis (e.g., such as origin or specific gravity or magnetism), the image classification model can provide a more reliable and accurate assessment when the primary identifiers selected take into account the type of data (imagery) which is being processed.”), training the machine learning engine (Vaidyanathan paragraph 25: “As shown in FIG. 2, both the soil-based data generated by soil processor 212 and image data generated by image processor module 222 are transmitted to and received by soil and image classification and analytics module (“classification module”) 272. The classification module 272 is configured to, among other thing, execute one or more image classification models built using Deep Learning Open Source Frameworks such as Python callable libraries and other Machine Learning Applications, including Convolution Neural Networks (CNNs) using TensorFlow, Keras, Microsoft® CNTK, OpenCV and/or Clarifai API, Custom Image Classification Models, or other similar techniques known in the art, including but not limited to Theano, Torch, Caffe, SciKit-Learn, Accord.NET, Spark MLib, Azure® ML Studio, Amazon® Machine Learning (AML), Google® Vision API, Image Processing and Computer Vision API (Microsoft® Azure), etc.”). It would have been obvious to one of ordinary skill in the art, at the time the filing was made to apply Vaidyanathan to Xu so the image vector set is balanced that would be better suited for training purposes to get better training result from a machine learning engine.
With regard to claims 10 and 20:
Xu and Vaidyanathan disclose the method according to claim 1, further comprising, providing the balanced training set (see fig. 5 for categories which include rock type such as Fe, Si, Al Mn, Ca, paragraph 46 and 47: “Referring now to FIG. 6, for purposes of reference, an array of geological identifiers 600 (i.e., physical properties) that may be used by the image classification model to identify mineral composition is presented. A first identifier category (“Origin”) 610 refers to whether a mineral is Igneous, Sedimentary, or Metamorphic, and a second identifier category (“Grain Size”) 612 refers to whether the mineral is fine, coarse, rounded, jagged, banded, or non-banded. In addition, a third identifier category (“Specific Gravity”) 614 refers to a ratio of a particular mineral's mass to that of mass of an equal volume of water and a fourth identifier category (“Magnetism”) 616 refers to the ability of the rock to attract metallic objects. A fifth identifier category (“Fracture/Cleavage”) 618 describes the way a mineral breaks, and a sixth identifier category (“Crystal Habit”) 620 refers to the commonly found shape or form of the mineral, such as Prismatic, Tabular, Acicular, or Fibrous. A seventh identifier category (“Streak”) 622 refers to the color left behind when the mineral is finely crushed, while an eighth identifier category (“Color”) 624 refers to the color of the mineral, such as reddish-brown (Hematite), green (Olivine), violet (Amethyst), etc. In addition, a ninth identifier category (“Hardness”) 626 is measured on the 10-point Mohs scale, a tenth identifier category (“Luster”) 628 refers to whether a mineral is Metallic, Submetallic, Adamantine, Glassy, Resinous, Waxy, or Pearly, and an eleventh identifier category (“Structure and Texture”) 630 refers to the size, shape, and arrangement of the grains or crystals, as well as the extent of homogeneity (i.e., uniformity of composition throughout). The descriptor concepts identified for each category above should be understood to serve primarily of examples, and non-limiting. For example, concept names or labels may be customized for the particular mining operation, or include additional labels in some cases and fewer in others. One or more of these identifier categories can be fed into the image classification model as a basis by which the model may determine an identity of the mineral(s) present in a captured image. While a wide range of physical properties may be utilized by the system as identifier categories (see FIG. 6), it may be appreciated that the image classification model may offer greater accuracy when relying on physical properties that are more readily discernable through images. In other words, in seeking to remotely identify minerals, in order to classify certain physical properties of the mineral, the collection of physical or tangible samples may be required for testing for physical properties of the samples. For example, while some physical properties might be better detected based on XRF information or other laboratory-type analysis (e.g., such as origin or specific gravity or magnetism), the image classification model can provide a more reliable and accurate assessment when the primary identifiers selected take into account the type of data (imagery) which is being processed.”) to the machine learning engine to train the machine learning engine (Vaidyanathan paragraph 25: “As shown in FIG. 2, both the soil-based data generated by soil processor 212 and image data generated by image processor module 222 are transmitted to and received by soil and image classification and analytics module (“classification module”) 272. The classification module 272 is configured to, among other thing, execute one or more image classification models built using Deep Learning Open Source Frameworks such as Python callable libraries and other Machine Learning Applications, including Convolution Neural Networks (CNNs) using TensorFlow, Keras, Microsoft® CNTK, OpenCV and/or Clarifai API, Custom Image Classification Models, or other similar techniques known in the art, including but not limited to Theano, Torch, Caffe, SciKit-Learn, Accord.NET, Spark MLib, Azure® ML Studio, Amazon® Machine Learning (AML), Google® Vision API, Image Processing and Computer Vision API (Microsoft® Azure), etc.”). It would have been obvious to one of ordinary skill in the art, at the time the filing was made to apply Vaidyanathan to Xu so the image vector set is balanced that would be better suited for training purposes to get better training result from a machine learning engine.
Claim 11 is rejected for the same reason as claim 1.
Claim(s) 7 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu, CN 111563445 A, in view of Vaidyanathan, US11315228B2, and further in view of Francois et al, Pub. No.: 20210319257 A1.
With regard to claim 7 and 17:
Xu and Vaidyanathan do not disclose the method according to claim 1, wherein each of the images is linked to a depth in the geological formation at which a respective cutting was obtained.
However Francois discloses the aspect wherein each of the images is linked to a depth in the geological formation at which a respective cutting was obtained. (paragraph 65: “The known rock sample may comprise drill cuttings. In such implementations, among others within the scope of the present disclosure, the unknown rock sample may be one of a plurality of unknown rock samples each retrieved from a different depth in the geological formation and depicted in a corresponding one of a plurality of unknown rock sample images, and the method may further comprise: performing the sub-class ascription, the meta-class derivation, and the at least one property prediction utilizing each of a plurality of unknown rock sample images; and generating a lithology profile of the geologic formation versus depth based on the property predictions.”). It would have been obvious to one of ordinary skill in the art, at the time the filing was made to apply Francois Xu and Vaidyanathan so images are organized based on depth and the images and the machine training can yield result that can provide insight on the rock types with respect to different depth and the user can be more informed about the material distribution of the geological region.
Pertinent Arts
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Al-Qubaisi, Pub. No.: 20230374903 A1: A computer-implemented method that autonomously performs rock drill cuttings interpretation is described herein. The method includes obtaining rock drill cuttings representations. The method also includes preprocessing the rock drill cuttings representations. The method also includes performing unsupervised image segmentation in order to obtain masked representations of such images discriminating rock types. The method also includes performing supervised learning through a custom Convolutional Neuronal Network using the segmented pictures as inputs and a continuous or discrete mineralogical or sedimentological variable of interest as the output. Additionally, the method includes autonomously predicting such mineralogical or sedimentological quantity from new rock drill cuttings pictures using the parameters of the unsupervised segmentation and the trained supervised model created for this purpose.
BARTON, Pub. No.: US 20230154150 A1: The present disclosure refers to methods and system for classifying rocks according to their minerals from the processing of color images and hyperspectral images. The methods and systems of this disclosure achieve an efficient and low-cost classification of rocks with minerals. In particular, the method uses classification methods that take color images and hyperspectral images and give as a result a probability that the rock or rocks present in said images are suitable or waste.
Conclusion
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/DI XIAO/Primary Examiner, Art Unit 2178