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 . Claims 1-20 are pending.
Drawings
The drawings are objected to because Figs. 4A-12 appear to be informal screenshots of a computer screen rather than high-resolution, high-contrast line drawings or compliant photographs. Pursuant to MPEP § 608.02, patent illustrations must be of sufficient quality and clarity to permit effective reproduction and examination.
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. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. 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.
Claim Objections
Claims 18-20 are objected to because of the following informalities:
Claims 18-20 recite “The method according to claim 16” in the preamble; however, it’s clear that the claims are supposed to depend on claim 17. The preamble should read: “The method according to claim 17.”
Given that the metes and bounds are otherwise clear, the claims have not been rejected for indefiniteness. Still, appropriate correction is required.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
In claims 1, 10, and 17, applicant recites “determining a recipe to score a set of the ultrasonic images of the downhole environment” and “using the recipe to score and select a resulting dataset of ultrasonic images.”
Examiner has not been able to determine, based on applicant’s disclosure, how a recipe is determined or how a recipe is used to score and select a resulting dataset of ultrasonic images. Applicant’s disclosure contains no mention of a “recipe” or its role in the accompanying steps. The only reference to “scoring” is in [0050] of applicant’s specification as filed, and that pertains to a “a probability or confidence level,” not a description of how a recipe is determined or how a recipe is used to score and select a resulting dataset of ultrasonic images. In effect, applicant has claimed a black box, where the written description fails to reasonably convey to one skilled in the relevant art that the inventor had possession of the claimed invention, at the time of filing.
The dependent claims are rejected by virtue of their dependency. Accordingly, claims 1-20 are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. 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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1 (The Statutory Categories): Is the claim to a process, machine, manufacture, or composition of matter? MPEP 2106.03.
Per Step 1, claims 1 and 17 is to a method (i.e., a process), claim 10 an article of manufacture (i.e., a manufacture). Thus, the claims are directed to statutory categories of invention. However, the claims are rejected under 35 U.S.C. 101 because they are directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The analysis proceeds to Step 2A Prong One.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon? MPEP 2106.04.
The abstract idea of claims 1 and 10 is:
selecting a training dataset;
generating labels and classes from the selected training dataset;
obtaining a model, the selected training dataset and the generated labels and classes;
using the obtained model to evaluate eccentering from the ultrasonic images;
obtaining ultrasonic images related to a downhole environment;
determining a recipe to score a set of the ultrasonic images of the downhole environment;
using the recipe to score and select a resulting dataset of ultrasonic images; and
updating the training dataset with the resulting dataset of ultrasonic images.
The abstract idea of claim 17 is:
selecting an ultrasonic image training dataset;
generating labels and classes from the selected training dataset;
obtaining a model, the selected training dataset and the generated labels and classes;
using the obtained model to evaluate eccentering from the ultrasonic images;
obtaining ultrasonic field images related to a downhole environment;
determining a recipe to develop a score for analyzed images;
using the recipe to score and select a resulting analyzed dataset of ultrasonic images; and
updating the training dataset with the resulting dataset of ultrasonic images.
The abstract idea steps italicized above are those which could be performed mentally and/or manually. The steps describe, at a high level: obtaining ultrasonic images, using the recipe to score and select a resulting analyzed dataset of ultrasonic images, and updating the training dataset with the resulting dataset of ultrasonic images. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, including observations, evaluations, judgements, and/or opinions, then it falls within the Mental Processes – Concepts Performed in the Human Mind grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally and alternatively, the abstract idea steps italicized above describe the rules or instructions pertaining to building a training dataset, which constitutes a process that, under its broadest reasonable interpretation, covers managing personal behavior relationships, interactions between people. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior relationships, interactions between people, including social activities, teaching, and/or following rules or instructions, then it falls within the Certain Methods of Organizing Human Activity – Managing Personal Behavior Relationships, Interactions Between People grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? MPEP 2106.04.
This judicial exception is not integrated into a practical application because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP 2106.05(f).
Claim 1 recites the following additional elements: through artificial intelligence from a pretrained computer model.
Claim 10 recites the following additional elements: an article of manufacture comprising a non-volatile memory, the non-volatile memory configured to store a list of instructions configured to be read by a computer; through artificial intelligence from a pretrained computer model
Claim 17 recites the following additional elements: through artificial intelligence from a pretrained computer model.
These elements are merely instructions to apply the abstract idea to a computer, per MPEP 2106.05(f). Applicant has only described generic computing elements in their specification, as seen in [0046] and [0054] of applicant’s specification as filed, for example.
Further, the combination of these elements is nothing more than a generic computing system applied to the tasks of the abstract idea. Because the additional elements are merely instructions to apply the abstract idea to a generic computing system, they do not integrate the abstract idea into a practical application, when viewed in combination. See MPEP 2106.05(f).
Therefore, per Step 2A Prong Two, the additional elements, alone and in combination, do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
Step 2B (The Inventive Concept): Does the claim recite additional elements that amount to significantly more than the judicial exception? MPEP 2106.05.
Step 2B involves evaluating the additional elements to determine whether they amount to significantly more than the judicial exception itself.
The examination process involves carrying over identification of the additional element(s) in the claim from Step 2A Prong Two and carrying over conclusions from Step 2A Prong Two pertaining to MPEP 2106.05(f).
The additional elements and their analysis are therefore carried over: applicant has merely recited elements that facilitate the tasks of the abstract idea, as described in MPEP 2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system applied to the tasks of the abstract idea. When the claim elements above are considered, alone and in combination, they do not amount to significantly more.
Therefore, per Step 2B, the additional elements, alone and in combination, are not significantly more. The claims are not patent eligible.
The analysis takes into consideration all dependent claims as well:
Dependent claims 2-9, 11-16, and 18-20 recite additional abstract steps and/or information that further narrow the abstract idea(s) above. This does not integrate the abstract idea into practical application and/or add significantly more. Some of the dependent claims recite further additional elements, beyond those highlighted above:
Claims 4, 15, and 18: wherein the model obtained through artificial intelligence is a neural network.
Claims 7 and 20: in a non-volatile memory.
Claim 11: wherein the non-volatile memory is configured as one of a mass storage device, a universal serial device, a solid-state device, a computer hard disk, a compact disk, and a computer server.
Similar to above, these are generic computing elements that are merely facilitating the tasks of the narrowed abstract idea(s). Applicant has only described generic computing elements in their specification, as seen in [0025], [0046], and [0054] of applicant’s specification as filed, for example. Whether viewed alone or in combination, this does not integrate the narrowed abstract idea(s) into practical application and/or add significantly more. See MPEP 2106.05(f).
Accordingly, claims 1-20 are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
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.
Claims 1, 5-12, 16-17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Bose (US 20180149019) in view of Hurley (US 20090262603).
Claims 1, 10, and 17
[A method to analyze data related to ultrasonic images {[0008], [0057]}, comprising:]
[An article of manufacture comprising a non-volatile memory, the non-volatile memory configured to store a list of instructions configured to be read by a computer, the list of instructions comprising a method to analyze data related to ultrasonic images {[0008], [0057], [0123]}, comprising:]
[A method to analyze data related to ultrasonic images obtained from a downhole environment {[0008], [0039], [0057]}, the data including third interface echo information {[0085]}, the method comprising:]
generating labels and classes from the selected training dataset {generating labels and classes from the selected training dataset: [0087] The training of the machine learning classifier can be posed as a classification problem, with the inputs being the phase and attenuation dispersion features indicative of various scenarios of annulus fill states and bonding states, and the outputs (“classes” or “labels”) being the state of the annulus fill and bond.};
obtaining a model through artificial intelligence from a pretrained computer model, the selected training dataset and the generated labels and classes {obtaining a model through artificial intelligence from a pretrained computer model, the selected training dataset and the generated labels and classes: [0071] Such features as derived from synthetic sonic data can be used to train the machine learning classifier in a supervised fashion such that the trained machine learning classifier outputs classes that correspond to such features, where the classes pertain to properties (such as the fill state and bond state) of the innermost annulus and the at least one outer annulus of the multiple casing string for the given azimuth direction φ and axial depth z for a variety of formations. These classes can be used to characterize and diagnosis the condition of placed cement in both the innermost annulus and the at least one outer annulus of the multiple casing string for the given azimuth direction φ and axial depth z. The trained machine learning classifier can be used in an unsupervised fashion with features derived from the multimode sonic measurements of the sonic logging tool for the given azimuth direction φ and axial depth z of the multiple casing string in conjunction with the properties of the innermost annulus as obtained from the integrated inversion methodology as described above for the given azimuth direction φ and axial depth z (collectively, field data) to output classes that correspond to such features for the given azimuth direction φ and axial depth z. The classes output by the trained machine learning classifier pertain to properties (such as the fill state and bond state) of the innermost annulus as well as the at least one outer annulus (and beyond) of the multiple casing string for the given azimuth direction φ and axial depth z as investigated by the ultrasonic and sonic logging tools. The trained machine learning processing can be applied over the range of azimuth directions φ and axial depths z of the multiple casing string investigated by the ultrasonic and sonic logging tools where the classes output by the trained machine learning classifier can be used to characterize properties (such as the fill state and bond state) of the innermost annulus as well as the at least one outer annulus (and beyond) of the multiple casing string over the range of azimuth directions φ and axial depths z of the multiple casing string as investigated by the ultrasonic and sonic logging tools.};
using the obtained model to evaluate eccentering from the ultrasonic images {using the obtained model to evaluate eccentering from the ultrasonic images: [0108] The training of the machine learning classifier of block 1403 can also account for the effect of casing eccentering on phase and attenuation dispersion characteristics of the sonic data.}.
Bose doesn’t explicitly disclose, however, Hurley, in a similar field of endeavor directed to characterizing borehole formations and selecting training images, teaches:
selecting [an ultrasonic image] training dataset {selecting [an ultrasonic image] training dataset: [0076] Therefore, according to the invention, the training image that will be selected represents a depth-defined interval of the borehole-image log. For example, this interval could be 1, 3, or 10 ft (0.3, 1, or 3 m) of measured depth. The user may want to choose a thick or thin interval, depending on the observed amount of layering, fracturing, and other heterogeneous patterns.
ultrasonic images also described in [0013]: The UBI (Ultrasonic Borehole Imager) is Schlumberger's primary acoustic tool for open-hole applications.};
obtaining ultrasonic images related to a downhole environment {obtaining ultrasonic images related to a downhole environment: [0066] As explained earlier, electrical borehole images in water-based (conductive) and oil-based (non-conductive) muds are generated from electrodes arranged in fixed patterns on pads that are pressed against the borehole wall. Also see [0013].};
determining a recipe to score a set of the ultrasonic images of the downhole environment [to develop a score for analyzed images] {determining a recipe to score a set of the ultrasonic images of the downhole environment [to develop a score for analyzed images], where recipe represented by user-selected template: [0080] Once the training image is selected, the method will determine filter scores to categorize and classify the observed patterns. To do this, the human user of the method according to the invention chooses a suitable template. For example, the template could be 3.times.3, 3.times.10, or 9.times.9 pixels. This template is used as a filter that moves through the measured data and records all possible patterns and assigns scores to them for further classification and simulation.};
using the recipe to score and select a resulting [analyzed] dataset of ultrasonic images {using the recipe to score and select a resulting [analyzed] dataset of ultrasonic images: [0081] Once filter scores are determined for each training image using a suitable pixel-based template, the method according to the invention uses these filter scores to group and then simulate patterns in the gaps between the pads, where no measured data exist.}; and
updating the training dataset with the resulting dataset of ultrasonic images {updating the training dataset with the resulting dataset of ultrasonic images: [0078] For illustration, the pixel-based, user-defined 3.times.3 template 8 as showed at the bottom of FIG. 7 is moved through the training image, detecting patterns and giving filter scores to the neighbourhoods around each measured pixel. In one exemplary embodiment of this invention, this provides the basis for MPS simulation using the FILTERSIM algorithm of FIG. 6. However, FILTERSIM was taken as example of algorithm only. Other algorithms that perform pattern-based simulation using the original (incomplete) logged images as training images could replace FILTERSIM to create fullbore images.}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Bose to include the features of Hurley. Given that Bose is directed to analyzing ultrasonic data using machine learning, one of ordinary skill in the art would have been motivated to look to Hurley, in order to facilitate "filling the gaps" between the pads of borehole image logs with modeled images, thereby streamlining training image generation {[0031], [0032] of Hurley}.
Claims 5 and 16
Bose further discloses: wherein the ultrasonic images are from a field operation {[0011] The training of the machine learning classifier can use synthetic sonic data (computed with one or more modeling algorithms) in supervised learning mode as well as field data in unsupervised or semi-supervised mode. Also see [0071].}.
Claim 6
Hurley further teaches: further comprising updating the training dataset using the resulting dataset of ultrasonic images {See previous citation to [0078].}.
The motivation and rationale to include the additional features of Hurley is the same as set forth previously.
Claims 7 and 20
Hurley further teaches: further comprising saving the updated training dataset in a non-volatile memory [saving one of the updated training dataset in a non-volatile memory and displaying the updated training dataset] {[0063] Furthermore, embodiments of the invention may be implemented, at least in part, either manually or automatically. Manual or automatic implementations may be executed, or at least assisted, through the use of machines, hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof. When implemented in software, firmware, middleware or microcode, the program code or code segments to perform the necessary tasks may be stored in a machine readable medium. A processor(s) may perform the necessary tasks. Also see [0009]: Typically, borehole images are created by assigning colour maps to different bins or ranges of resistivity values. Colour pixels are then arranged in their proper geometric position around the wellbore. By convention, low-resistivity features, such as shales or fluid-filled fractures, are displayed as dark colours. High-resistivity features, such as sandstones and limestones, are displayed as shades of brown, yellow, and white (FIG. 4, representing small-scale fault, or microfault (M), and bed boundaries (B) in a sand and shale interval).}.
The motivation and rationale to include the additional features of Hurley is the same as set forth previously.
Claim 8
Bose further discloses: wherein the training dataset is pulse-echo data {[0014] In some embodiments, the ultrasonic measurements can include ultrasonic pulse echo and pitch-catch measurements. }.
Claims 9 and 12
Bose further discloses: wherein the training dataset includes third interface echo data {[0085] The inversion or interpretation of block 1007 can characterize the first casing eccentering (with respect to second casing) using the third interface echo (TIE) obtained from the pitch-catch flexural signal of the ultrasonic logging tool.}.
Claim 11
Bose further discloses: wherein the non-volatile memory is configured as one of a mass storage device, a universal serial device, a solid-state device, a computer hard disk, a compact disk, and a computer server {[0123] The processing system may further include a memory such as a semiconductor memory device (e.g., a RAM, ROM, PROM, EEPROM, or Flash-Programmable RAM), a magnetic memory device (e.g., a diskette or fixed disk), an optical memory device (e.g., a CD-ROM), a PC card (e.g., PCMCIA card), or other memory device.}.
Claims 2-3, 13-14, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Bose and Hurley, further in view of “A deep learning workflow for weak reflection extraction in pitch-catch measurements in the cased hole” by Wang et al. (NPL provided by applicant; hereinafter Wang).
Claims 2, 13, and 19
The combination of Bose and Hurley doesn’t explicitly teach, however, Wang, in a similar field of endeavor directed to cased hole measurements, teaches: wherein the selecting of the training dataset is from a synthetic waveform dictionary {See “Methods” on page D148: The proposed deep learning workflow consists of three stages (see Figure 2): initial training, active learning, and arrival-time autopicking. We first use synthetic waveforms from finite-difference time-domain (FDTD) simulations (Wang et al., 2013) to train an initial deep learning model.
Also see “Results” on page D150: Because the proposed deep learning network is a supervised method, we need to build a training data set and design a loss module to control the performance. The training data sets are obtained by subtracting the FDTD-simulated waveform in a three-layer model (the media from the innermost to the outermost are fluid, casing, and cement or fluid) from the FDTD-simulated waveform in a four-layer model (the media from the innermost to the outermost are fluid, casing, cement or fluid, and formation). We acquire 3893 labeled waveform data from the following investigations in the simulations:
two casing sizes (9.625 and 13.375 in)
annulus materials (water or cement)
eccentered casing (eccentricities: 0, 4, and 22 mm)
rotation measurements (36 interval for a 360° coverage in borehole azimuth)
eccentered tool (eccentricities: 0, 5, 10, 15, 20, 25, 30, 35, 40, 45, and 50 mm)
offsets (25, 26, 27, 28, 29, 30, 31, 32, 33, 34, and 35 cm).}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the combination of Bose and Hurley to include the features of Wang. Given that Bose is directed to analyzing ultrasonic data using machine learning, one of ordinary skill in the art would have been motivated to look to Wang, in order to facilitate accurately picking or extracting the third-interface-echo (TIE) modes in noisy data {See “Introduction” on pages D147, D148 of Wang}.
Claims 3 and 14
Wang further teaches: wherein the synthetic waveform dictionary has eccentering and azimuth combinations {See previous citations to Wang in claims 2, 13, and 19}.
The motivation and rationale to include the additional features of Wang is the same as set forth previously.
Claims 4, 15, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Bose and Hurley, further in view of Gkortsas (US 20210032973).
Claims 4, 15, and 18
The combination of Bose and Hurley doesn’t explicitly teach, however, Gkortsas, in a similar field of endeavor directed to evaluating casing cement, teaches: wherein the model obtained through artificial intelligence is a neural network {[0036] One embodiment of a CNN for detecting galaxy patterns is shown in FIG. 8. The CNN takes as input, images which are frames of impedance maps and gives as output the probability that the image has a galaxy pattern (p1) or not (p0).}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the combination of Bose and Hurley to include the features of Gkortsas. Given that Bose is directed to analyzing ultrasonic data using machine learning, one of ordinary skill in the art would have been motivated to look to Gkortsas, in order to facilitate automatic feature extraction via a neural network {[0035] of Gkortsas}.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
“Automatic interpretation of cement evaluation logs from cased boreholes using supervised deep neural networks” (NPL attached), which teaches: This system is based on deep convolutional neural networks, which we train in a supervised manner using a dataset of around 60 km of interpreted well log data. Thus, the networks learn the connections between data and interpretations during training. More specifically, the task of the networks is to classify the bond quality (among 6 ordinal classes) and the hydraulic isolation (2 classes) in each 1m depth segment of each well based on the surrounding 13 m of well log data.
US 20150219780, which teaches: Apparatus and method for characterizing a barrier installed in a borehole traversing a formation including locating an acoustic tool with a receiver and a transmitter at a location in the borehole, activating the acoustic tool to form acoustic waveforms, wherein the receiver records the acoustic waveforms, and processing the waveforms to identify barrier parameters as a function of azimuth and depth along the borehole, wherein the waveforms comprise at least two of sonic signals, ultrasonic pulse-echo signals, and ultrasonic pitch-catch signals.
US 20200072999, which teaches: Methods for correcting eccentering effects on echoes detected from ultrasonic pulses emitted by a transducer of a downhole tool. Echo envelope amplitude, azimuth, and location for each echo is utilized to assess echo amplitude sensitivity to geometric and spatial characteristics of the downhole tool within the wellbore. Echo envelope amplitudes are corrected for eccentering effects based on the assessed sensitivity. A visual representation of the corrected echo envelope amplitudes is the generated. Also disclosed herein are tangible, non-transient, computer-readable media comprising instructions executable by a processor to carry out the methods, as well as systems including downhole tools and processing devices operable to carry out the methods.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN SAMUEL WASAFF whose telephone number is (571)270-5091. The examiner can normally be reached Monday through Friday 8:00 am to 6:00 pm.
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, SARAH MONFELDT can be reached at (571) 270-1833. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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JOHN SAMUEL WASAFF
Primary Examiner
Art Unit 3629
/JOHN S. WASAFF/Primary Examiner, Art Unit 3629
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