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 .
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description:
Specifically, paragraph 44 refers to element “716” in connection with figure 7; However, figure 7 does not include a reference numeral “716”. This inconsistency between the specification and the drawings renders the disclosure unclear.
Figure 9 does not show reference numerals 902, 904, 906, and 908, although paragraphs [45-48] refer to those numerals when describing the steps of figure 9.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
Specifically, in paragraph [23], the term “can 102” appears to be a typographical error and should be corrected to “scan 102” to be consistent with the disclosure.
Additionally, in paragraph [32], the reference to “Saood” appears to be editorial content and is not part of the disclosure of the invention. Such content should be removed to ensure the specification is free of informal remarks.
Appropriate correction is required.
Claim Rejections - 35 USC § 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.
Claim Claims 1-2 and 4-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite an abstract idea as discussed below. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the reasons discussed below.
Step 1 of the 2019 Guidance requires the examiner to determine if the claims are to one of the statutory categories of invention. Applied to the present application, claims 1-20 are directed to methods, computer readable media, and systems for digitizing well log curves using machine learning techniques which fall within one of the statutory categories of invention (Claims 1-10, process), (claims 11-19, manufacture) and (claim 20, machine) under 35 U.S.C. §101.
Step 2A prong 1:
Claims 1-10, 11 and 20 are reproduced below with the abstract idea underlined. Claims 12-19 are computer readable claims with e the same limitations as claim 1-10 and 11.
Claim 1: A machine learning method of digitizing a well log curve, the method comprising: obtaining a scan of a well log curve paper document; providing the scan of the well log curve paper document to a trained segmentation neural network, wherein a curve mask is obtained; providing the curve mask to a trained digitization neural network, wherein a digitization of the curve mask is obtained; and outputting the digitization of the curve mask.
Claim 2: The method of claim 1, wherein the outputting comprises outputting to a drilling process.
Claim 3: The method of claim 2, further comprising performing a drilling operation based on the digitization of the curve mask.
Claim 4: The method of claim 1, wherein at least one of the trained segmentation neural network or the trained digitization neural network is trained using synthetic training data, wherein the synthetic training data is generated from numerical coordinate data.
Claim 5: The method of claim 4, wherein the trained segmentation neural network is trained using the synthetic training data, wherein each synthetic training datum comprises a respective pair, wherein each respective pair comprises an image of a respective curve generated from respective numerical coordinate data and the image of the respective curve generated from respective numerical coordinate data combined with a respective grid depiction, and wherein the trained segmentation neural network is trained to remove a grid from the scan of the well log curve paper document.
Claim 6: The method of claim 4, wherein the trained digitization neural network is trained using the synthetic training data, wherein each synthetic training datum comprises a respective pair, and wherein each respective pair comprises a section of an image of a respective curve generated from respective numerical coordinate data and a corresponding respective numerical position value.
Claim 7: The method of claim 4, wherein both of the trained segmentation neural network and the trained digitization neural network are trained using the synthetic training data.
Claim 8: The method of claim 1, wherein the scan of the well log curve paper document comprises a plurality of different curves, and wherein the trained segmentation neural network is trained to produce the curve mask for a selected line style.
Claim 9: The method of claim 8, further comprising annotating the scan of the well log curve paper document with the selected line style prior to the providing the scan of the well log curve paper document to the trained segmentation neural network.
Claim 10: The method of claim 1, wherein the providing the scan of the well log curve paper document to the trained segmentation neural network comprises dividing the scan of the well log curve paper document into a plurality of scan tiles and providing the scan tiles individually to the trained segmentation neural network, wherein the curve mask comprises a plurality of curve mask tiles, and wherein the providing the curve mask to the trained digitization neural network comprises providing the curve mask tiles individually to the trained digitization neural network, wherein the digitization of the curve mask comprises a plurality of digitizations of the curve mask tiles.
Claim 11: A non-transitory computer-readable medium comprising instructions that, when executed by an electronic processor, configure the electronic processor to digitize a well log curve by performing actions comprising: obtaining a scan of a well log curve paper document; passing the scan of the well log curve paper document to a trained segmentation neural network, wherein a curve mask is obtained; passing the curve mask to a trained digitization neural network, wherein a digitization of the curve mask is obtained; and outputting the digitization of the curve mask.
Claim 20: A system comprising an electronic processor and a non-transitory computer-readable medium comprising instructions that, when executed by the electronic processor, configure the electronic processor to digitize a well log curve by performing actions comprising: obtaining a scan of a well log curve paper document; passing the scan of the well log curve paper document to a trained segmentation neural network, wherein a curve mask is obtained; passing the curve mask to a trained digitization neural network, wherein a digitization of the curve mask is obtained; and outputting the digitization of the curve mask.
Claims 1-20 recite limitations that describe processing and analyzing data using mathematical models, including applying these models to transform image data into numerical representations. Such operations constitute mathematical concepts, as they involve computations and algorithms for data analysis and transformation. Additionally, steps of selecting, annotating, segmenting, and digitizing image data represent mental processes.
The dependent claims further recite additional data processing techniques, such as training neural networks using synthetic data (Claims 4-7, 13-16), selecting curves based on line style (claims 8 and 17), annotating input data (claims 9 and 18), and dividing data into tiles for processing (claims 10 and 19). These limitations also constitute data manipulation and analysis technique which fall within abstract idea.
Accordingly, claims 1-20 recite a judicial exception in the form of an abstract idea, namely processing and converting data using mathematical concepts and mental processes.
In Step 2A prong2: examiner needs to determine if the claim(s) recite additional elements that integrate the exception into a practical application of the exception.
The additional elements in the claim have been left in normal font. Claims do not integrate the judicial exception into a practical application because of the following reasons:
Claim 1: The step of obtaining a scan merely includes data collection, and the recitation of a well log curve paper document is a limitation to a particular technological environment. The segmentation neural network and the digitization neural network are recited at a high level of generality and perform their ordinary functions of processing data and generating outputs, without any improvement to computer functionality or neural network technology. The step of outputting the digitization is post-solution activity. Accordingly, the claim does not integrate the abstract idea into a practical application.
Claims 11 and 20: Claim 11 recites a non-transitory computer readable medium storing instruction, and claim 20 recites a system including a processor and a computer readable medium, both configured to perform the same steps recited in claim 1. The additional elements of a computer readable medium and processor are generic computing components that perform their ordinary functions of storing and executing instructions. The additional elements do not integrate the abstract idea into a practical application.
Claim 2: Outputting to a drilling process merely specifies the environment that the result is used and does not require any functional interaction with or control of the drilling process. This limitation amounts to a field-of-use restriction and does not impose a meaningful limit on the abstract idea.
Claim 4-7: The additional elements, trained neural networks, recited in high level of generality and perform their ordinary functions of processing data, and the use of synthetic training data merely describes how the models are trained. Accordingly, the additional elements do not impose a meaningful limit on the abstract idea.
Claim 8-10: Claim 8 recites selecting a curve based on a line style in a multi-curve image constitute data selection and classification based on visual attributes, claim 9 recites annotating the scan, and claim 10 recites dividing the scans into tiles and processing the tiles individually. The additional limitations do not improve computer functionality or neural network technology. These claims do not apply the abstract idea in a meaningful way beyond its implementation on generic computing components.
Claims 12-19: These claims recite the same additional elements as corresponding method claims 2, 4-10 and independent claim 11. Accordingly, these claims do not integrate the judicial exceptions into a practical application for the same reasons discussed with respect to the corresponding preceding claims.
The additional elements in claims 1-2 and 4-20, individually and in combination, amount to no more than well understood, routine, and normal activities and therefore do not provide an inventive concept under step 2B. Accordingly, claims 1-2 and 4-20 also fail Step 2B analysis.
Regarding claim 3: Unlike merely outputting data, claim 3 requires using the processed data to affect a physical operation, namely a drilling operation, which constitutes a meaningful application of the abstract idea. Accordingly claim 3 integrates the abstract idea into a practical application.
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 non-obviousness.
Claims 1-9, 11-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over WO2021178412 hereinafter Katole in view of US20220010675 hereinafter Yin.
Regarding claim 1 Katole teaches a machine learning method of digitizing a well log curve (method for digitizing a raster image¶ [10], well log¶ [80], curves ¶ [6] and fig. 4A), the method comprising: obtaining a scan of a well log curve paper document (block 802, fig. 8, ¶ [58]);
Katole teaches utilizing a machine learning model for its segmentation and digitization steps, and furthermore characterizes its machine learning model as deep learning module (¶ [18]).
However, Katole does not disclose that the machine learning model is a neural network.
Katole does not teach providing the scan of the well log curve paper document to a trained segmentation neural network, wherein a curve mask is obtained; providing the curve mask to a trained digitization neural network, wherein a digitization of the curve mask is obtained; and outputting the digitization of the curve mask.
Yin teaches that image analysis-based well log data generation (fig.2 and ¶ [8]), including feature identification and translation of image data into numerical values (fig. 2 and ¶ [34]), may be implemented using neural networks (fig. 2).
It would have been obvious to a person having ordinary skill in the art before the
effective filing date of the claimed invention to implement the machine learning model of Katole using neural networks as taught by Yin because neural networks improve the accuracy of the curve identification and enable reliable translation of image-based representations into numerical data.
Katole in view of Yin further teaches providing the scan of the well log curve paper document (Katole, blocks 804 and 806, fig. 8) to a trained segmentation neural network (Katole, object segmentation machine learning model ((model 530 and module 426 and 428) and ¶ [4-6])), wherein a curve mask is obtained (block 406 fig.4A, extracted curves fig.5 and ¶ [69]);
Katole teaches the distinct steps of segmentation and digitization as a sequence within its machine learning workflow.
However, Katole does not explicitly discloses utilizing distinct machine learning models for segmentation and digitization.
Yin further teaches that its system 200 utilizes multiple distinct neural networks (fig2. Block 270 and ¶ [55]) to perform separate functions where the task of segmentation (identifying and isolating a curve) is handled by the image analysis engine 250 (fig. 2) and digitization is handled by the image decoder 280 (fig. 2) in conjunction with neural network 270 that is trained based on a set of training data 230 (fig. 2).
It would have been obvious to a person having ordinary skill in the art before the
effective filing date of the claimed invention to implement the machine learning model of Katole for segmentation and digitizing using distinct neural networks as taught by Yin because utilizing distinct network allows each model to be specialized for its specific goal, rather than using a single generic model that may be less efficient at both.
Katole in view of Yin therefore teaches providing (Katole, block 808 provides its output directly to block 810, fig 8) the curve mask (Katole, extracted curves fig. 5) to a trained (¶ [16] and ¶ [59]) digitization neural network (Katole, pixel to unit convertor 446 fig. 4B or the object extraction (machine learning) model 530 fig. 5 performing discretization), wherein a digitization of the curve mask is obtained (Katole, block 408, fig. 4A); and outputting the digitization of the curve mask (Katole, block 448 fig. 4B or block 812 fig. 8).
Claims 11 and 20 recite limitations similar to those in claim 1, but are directed to a computer readable medium and a system, respectively, comprising a processor configured to perform the recited method steps. Katole in view of Yin teaches a computing system including one or more processors and memory configured to perform the digitization method (Katole, fig 5 and fig 9) as well as a non-transitory computer readable medium storing instructions executable by a processor to perform the same operations (Katole, storage media 906, fig 9 and ¶ [90]). Inclusion of a processor and memory to execute the method steps represents no more than the implementation of the method on generic computer system, which would have been obvious to a person of ordinary skill in the art. Therefore, claims 11 and 20 are rejected for the same reasons as claim 1.
Regarding claim 2, Katole in view of Yin further teaches wherein the outputting comprises outputting to a drilling process (Katole, using the outputting data for planning well locations, trajectory, and other well planning tasks ¶ [86], Katole also teaches Block 135 in fig 1B (digital output interface) for drilling tools (106 b, fig. 1B). Since well trajectory (¶ [30]) is the specific path a drill bit follows into the earth, planning that path is the functional equivalent of a drilling process. Having physical hardware such as drilling tool 106.2 (Katole, ¶ [25]) establishes that the system is designed to interface with actual drilling activity).
It would have been obvious to a person having ordinary skill in the art before effective filing date of the claimed invention to utilize the output of Katole’s in view of Yin digitization system in a drilling process, as Katole in view of Yin teaches using the generated data for well planning, including trajectory determination. Since well trajectory planning directly governs drilling operations, applying such output to a drilling process represents a predictable use of the generated data.
Claim 12 recites the same limitations as claim 2 in computer-readable form and is rejected for the same reasons discussed above.
Regarding claim 3, Katole teaches that the legacy well log data (output of their machine learning model which is a numerical value) may be employed for planning well locations and trajectory and equipment (¶ [86]). Katole also teaches a communication unit 134 (¶ [26]) that communicates with drilling tool to send command and to receive data. The surface unit 134 may collect and use data in real-time (¶ [31]) and it may also be provided with one or more controllers to actuate mechanism in oilfield (¶ [32]).
However, Katole does not explicitly teach (method of claim 2) comprising performing a drilling operation based on the digitization of the curve mask.
Yin teaches that geologist and miners require data in real-time to make a determination on drilling ¶ [21]. It further specifies that the numerical output is used by a geological entity for performing one or more mining operations ¶ [23].
It would have been obvious to a person having ordinary skill in the art before effective filing date of the claimed invention to modify the method of Katole in view of Yin to perform a drilling operation, as taught by Yin, in order to enable real-time decision-making and improve operational efficiency. Such a modification represents the application of known data-driven control of drilling operations to the system of Katole, yielding predictable results.
Katole in view of Yin therefore teaches the method of claim 2 further comprising performing a drilling operation based on the digitization of the curve mask.
Regarding claim 4, Katole discloses training machine learning models using training corpus (block 427 and 429, fig. 4B) and it specifies receiving training raster images (¶ [75]) and curve labels (¶ [77]) for training to its digitization system 520 where digitization refers to mapping discrete coordinates to pixels ¶ [72]. Katole does not teach using synthetic data for training of its machine learning method.
Katole does not teach wherein at least one of the trained segmentation neural network or the trained digitization neural network is trained using synthetic training data, wherein the synthetic training data is generated from numerical coordinate data.
Yin further teaches training the neural network based on images (¶ [54]) and it specifies that the system use training data 230 (fig. 2) to adjust weights and biases via back propagation. Yin further teaches that neural network 270 may perform a visual reconstruction of the well log to generate training data without presence of any historical data (¶ [36]). Yin also teaches that the log curve generator 240 takes dimensions in numerical format and transforms them into images to create training dataset (¶ [35]).
It would have been obvious to a person having ordinary skill in the art before effective filing date of the claimed invention to incorporate the synthetic data generation techniques of Yin into the training process of Katole in order to augment training databases, thereby improving the robustness and performance of the machine learning models.
Therefore, Katole in view of Yin teaches the method of claim 1, wherein at least one of the trained segmentation neural network (Katole, ( model 530 and module 426 and 428) and ¶ [4-6])) or the trained digitization neural network (Katole, pixel to unit convertor 446 fig. 4B or the object extraction (machine learning) model 530 fig. 5 performing discretization) is trained using synthetic training data (Yin, ¶ [36 & 54]), wherein the synthetic training data is generated from numerical coordinate data (Yin, ¶ [35]) .
Regarding claim 7 Katole in view of Yin teaches the method of claim 4, wherein both of the trained segmentation neural network and the trained digitization neural network are trained using the synthetic training data for the reasons set forth above with respect to claim 4.
Claim 13 and 16 have the same limitations as claim 4 and 7, respectively, in form of computer readable medium. Therefore, they are rejected for the reasons set forth with respect to claim 4.
Regarding claim 5, Katole in view of Yin teaches the method of claim 4, wherein the trained segmentation neural network is trained using the synthetic training data, wherein each synthetic training datum comprises a respective pair (Katole, pairs of raster images and labels ¶ [64]), wherein each respective pair comprises an image of a respective curve (Katole teaches utilizing pairs of raster images and labels for training of both machine learning models 426 and 428 where the training process involves receiving a training raster image ¶ [75] (the curve with grid noise) and an intermediate label with ground truth (the clean curve) ¶ [76] to teach the model to remove background artifacts.)
Katole does not teach generated from respective numerical coordinate data and the image of the respective curve generated from respective numerical coordinate data combined with a respective grid depiction, and wherein the trained segmentation neural network is trained to remove a grid from the scan of the well log curve paper document.
Yin further teaches a log curve generator 240 that is configured for generating visual curves from mathematical values ¶ [35] to create training set ¶ [36] and inputting the one or more dimensions (dimensions are in numerical format) into an image ¶ [35].
It would have been obvious to a person having ordinary skill in the art before effective filing date of the claimed invention to combine the training framework of Katole with the synthetic data generation of Yin to automatically produce the clean labels required by Katole training pipeline from existing numerical coordinate data because manual labeling (tracing curves) is time intensive.
Therefore, Katole in view of Yin teaches generated from respective numerical coordinate data (Yin, ¶ [35]) and the image of the respective curve (an intermediate label with ground truth (the clean curve) ¶ [76]) generated from respective numerical coordinate data (Yin, ¶ [35]) combined with a respective grid depiction (Katole, training raster image ¶ [75], provided as the input half of the pair includes noise such as grid lines. It also notes that the ground truth of the curve location and the noise are known a priori ¶ [76]), and wherein the trained segmentation neural network is trained to remove a grid from the scan (Katole, denoising model 925, ¶ [70]) of the well log curve paper document.
Claim 14 recites the same limitations as claim 5 in form of computer readable medium. Claim 14 is rejected for the reasons set forth with respect to claim 5.
Regarding claim 6 Katole in view of Yin teaches the method of claim 4, wherein the trained digitization neural network (Katole, pixel to unit convertor 446 fig. 4B or the object extraction (machine learning) model 530 fig. 5 performing discretization) is trained using the synthetic training data (Yin, ¶ [36 & 54]), wherein each synthetic training datum comprises a respective pair (Katole teaches utilizing pairs of raster images and labels for training),
Katole teaches utilizing pairs of training “intermediate raster image” (block 730, fig 7) that consist of extracted curve pixels ¶ [62 and 72] and “curve labels” (block 740, fig 7) as ground truth. The system extract curve segments as intermediate input and determines plot values for discrete points along a one or more curves ¶ [6 and 63].
Katole does not teach and wherein each respective pair comprises a section of an image of a respective curve generated from respective numerical coordinate data and a corresponding respective numerical position value.
Yin teaches that the network is provided with a specific window (a 128 pixel by 250 pixel image input that contains a curve) ¶ [51] and compares its output against a desired output y(x) which represents the exact numerical position value for that section of the curve ¶ [52]. The log curve generator 240 generates the images for training from mathematical values ¶ [35].
It would have been obvious to a person having ordinary skill in the art before effective filing date of the claimed invention to implement the structure taught by Yin in Katole training process to improve accuracy.
Katole in view of Yin teaches and wherein each respective pair comprises a section of an image of a respective curve (Yin, ¶ [51]) generated from respective numerical coordinate data (Yin, ¶ [35]) and a corresponding respective numerical position value (Yin, ¶ [52]).
Claim 15 recites the same limitations as claim 6 in form of computer readable medium. Claim 15 is rejected for the reasons set forth with respect to claim 6.
Regarding claim 8 Katole in view of Yin teaches the method of claim 1, wherein the scan of the well log curve paper document comprises a plurality of different curves (Katole, fig 6A and 6B, two curves 625 and 627) , and wherein the trained segmentation neural network (Katole, trained object segmentation model identifies objects (curves) in an intermediate image ¶ [79] and ¶ [83]) is trained to produce the curve mask for a selected line style (Katole, the header segment 624 provides information related to line style 626 and that this metadata is used to distinguish between individual target objects(curves) ¶[73 & 79]).
It would have been obvious to a person having ordinary skill in the art before effective filing date of the claimed invention to train the segmentation neural network of Katole in view of Yin to produce curve masks for a selected line style, as Katole in view of Yin teaches distinguishing between multiple curves in a well log using metadata such as line style. Training the segmentation model to identify and isolate curves based on such distinguishing features represents a predictable implementation of known image segmentation techniques for improving curve differentiation.
Claim 17 recites the same limitations as claim 8 in form of computer readable medium. Claim 17 is rejected for the reasons set forth with respect to claim 8.
Regarding Claim 9, Katole in view of Yin teaches the method of claim 8, further comprising annotating the scan of the well log curve paper document with the selected line style (Katole, Metadata 540 which includes information such as line style may be manually added to the system ¶ [77] and also user input for a line style¶ [73]) prior to the providing the scan of the well log curve paper document to the trained segmentation neural network (fig 4B illustrates user inputs entering the system as an initial part of the workflow).
It would have been obvious to a person having ordinary skill in the art before effective filing date of the claimed invention to annotate the scan with a selected line style prior to segmentation, as Katole in view of Yin teaches incorporating user-provided metadata, including line style, into the processing workflow. Providing such annotations before input to the segmentation model represents a predictable use of known preprocessing techniques to improve feature identification accuracy.
Claim 18 recites the same limitations as claim 9 in form of computer readable medium. Claim 18 is rejected for the reasons set forth with respect to claim 9.
Claims 10 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Katole in view of Yin and further in view of US10095926 B1 hereinafter Venter.
Regarding claim 10, Katole in view of Yin teaches the method of claim 1 and providing the scan of the well log curve paper document to the trained segmentation neural network
Katole in view of Yin does not teach the tilling architecture.
Katole in view of Yin does not teach wherein the providing the scan of the well log curve paper document to the trained segmentation neural network comprises dividing the scan of the well log curve paper document into a plurality of scan tiles and providing the scan tiles individually to the trained segmentation neural network, wherein the curve mask comprises a plurality of curve mask tiles, and wherein the providing the curve mask to the trained digitization neural network comprises providing the curve mask tiles individually to the trained digitization neural network, wherein the digitization of the curve mask comprises a plurality of digitizations of the curve mask tiles.
Venter teaches a hierarchical processing workflow, which utilizes tiles to handle the large scale of well log scans.
Venter teaches comprises dividing the scan of the well log curve paper document into a plurality of scan tiles (Fig 4 step 404, ¶ [29]) and providing the scan tiles individually to the neural network (the trace analysis engine 218 (can be implemented as neural network ¶ [182]) can assign tile locations within a graph ¶ [45]), wherein the curve mask comprises a plurality of curve mask tiles (describes processing tiles to produce clusters separated by blank spaces within each tile location ¶ [32]), and wherein the providing the curve mask to the neural network (digitizing contents of tiles by converting a position to a paired value ¶[8 & 49]) comprises providing the curve mask tiles individually (clusters assigned to the well log trace based on the probability factor for each tile ¶ [19 & 49]) to the neural network, wherein the digitization of the curve mask comprises a plurality of digitizations of the curve mask tiles (digitized data can be determined from each of the graphs(tiles) within the well log image and stored collectively in the storage medium).
It would have been obvious to a person having ordinary skill in the art before effective filing date of the claimed invention to incorporate the tiling architecture of Venter into the method of Katole in view of Yin in order to efficiently process large well log images and improve computational performance and scalability.
Katole in view of Yin and Venter teaches wherein the providing the scan of the well log curve paper document to the trained segmentation neural network comprises dividing the scan of the well log curve paper document into a plurality of scan tiles and providing the scan tiles individually to the trained segmentation neural network, wherein the curve mask comprises a plurality of curve mask tiles, and wherein the providing the curve mask to the trained digitization neural network comprises providing the curve mask tiles individually to the trained digitization neural network, wherein the digitization of the curve mask comprises a plurality of digitizations of the curve mask tiles.
Claim 19 recites the same limitations as claim 10 in form of computer readable medium. Claim 19 is rejected for the reasons set forth with respect to claim 10.
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Nasim et al. (J. Imaging, Volume 9, Issue 7, July 2023, Article 136) focuses on the specific neural network architecture used for curve discrimination.
US10776967 to Witte provides a foundational disclosure for the raster log digitization system and method that preceded pure deep-learning approaches.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SAEEDE NAFOOSHE whose telephone number is (571)272-8629. The examiner can normally be reached Monday-Friday 8:00 am -5:00pm.
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, Andrew Schecter can be reached at 571-272-2302. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/SAEEDE NAFOOSHE/ Examiner, Art Unit 2857
/ANDREW SCHECHTER/ Supervisory Patent Examiner, Art Unit 2857