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 Status
Claims 25-43, and 48 are pending and have been examined.
Claims 1-24, 44-47, 49-93 have been canceled.
CLAIM INTERPRETATION
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “engine configured to” in claim(s) 33-36, and 40.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claim 25-43 and 48 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 18/548407 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the claims are directed to the same core inventive concept of utilizing an image analysis engine and communication circuitry to identify surface features of the Earth consistent with subsurface hydrogen accumulation.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claim 25-43 and 48 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 18/940720 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the claims are directed to the same core inventive concept of utilizing an image analysis engine and communication circuitry to identify surface features of the Earth consistent with subsurface hydrogen accumulation.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
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 25-43 and 48 rejected under 35 U.S.C. 101 because the claimed invention is directed to patent-ineligible subject matter. The claim(s) recite(s) the abstract idea of using a trained image classification model to identify geological surface features consistent with subsurface hydrogen accumulation, which is a mathematical concept (operations of the ML classification model) and/or a mental process (evaluation of surface features by a geologist). This judicial exception is not integrated into a practical application because the additional elements re generic computer components that merely implement the abstract idea without improving computer functionality or otherwise integrating the abstract idea into a practical application. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the application of this abstract idea to the specific domain of subsurface hydrogen exploration constitutes no more than a field-of-use limitation. See Alice Corp. v. CLS Bank lnt'l, 573 U.S. 208 (2014); Recentive Analytics, Inc. v. Fox Corp., 123 F.4th 1359 (Fed. Cir. 2024).
STEP 1 – Statutory Category
Claim 25 is a process. Claim 33 is a machine. Claim 41 is a manufacture (computer program product). All claims fall within a statutory category.
STEP 2A, PRONG 1 – Judicial Exception:
Claim 25's core steps are: (1) receiving a target image; (2) identifying, using a trained image
classification model, whether the image contains hydrogen-indicative surface features; (3)
outputting an indication.
Step 2’s "identifying ... using a trained image classification model" is the crux of the invention. Under BRI, applying a trained ML model to classify an image encompasses mathematical concepts (mathematical operations implementing the model) and mental processes (the classification of image features could conceptually be performed by a trained expert reviewing satellite imagery). The identification of geological surface features consistent
with hydrogen accumulation is a type of evaluation or judgment. This recites an abstract
idea combining mathematical concepts (the trained classification model's operations)
and mental processes (geological feature evaluation). See Example 47 (anomaly
detection using ANN - abstract idea). Steps (1) and (3) are data gathering and output insignificant extra-solution activity.
Step 2A Prong 1: YES-claims 25, 33, 41 recite an abstract idea.
STEP 2A, PRONG 2 - Practical Application:
Additional elements beyond the abstract idea: communications circuitry (receive/output),
image analysis engine (processor and software), and the specific application to subsurface
hydrogen accumulation surface features.
The key question here is: does the application to geological hydrogen exploration integrate the
abstract idea into a practical application by improving a technology or technical field?
The specification does not describe an improvement to how the ML model itself works, how image processing is performed, or how computer functioning is improved. The ML techniques used (CNN, object detection, semantic segmentation, orthorectification) are all generic, well-known techniques merely applied to a new domain.
The claim does not specify any particular technical implementation that distinguishes it from routine application of a generic ML image classifier to satellite imagery. The specification describes the result as useful (identifying hydrogen targets) but does not describe a technical improvement to image processing, ML, or computer technology.
The specific target domain (subsurface hydrogen) is a field-of-use limitation. It identifies what is being classified, not how the classification achieves a technical improvement. See MPEP §2106.05(h). This is analogous to Example 47, Claim 2 (ANN for anomaly detection found ineligible). Example 47, Claim 2 disclosed a generic application of ANN to a new detection domain without technical improvement to the underlying technology is insufficient. See also, Recentive Analytics v. Fox Corp. (Fed. Cir. 2024) that found applying generic ML techniques to a new domain does not render claims patent-eligible; the claims must reflect a concrete technical improvement to the technology itself, not merely improved results.
The "trained image classification model" is recited generically. There is no particular architecture, training methodology, or technical implementation beyond "CNN" (claims 30 & 38) is required by the independent claims.
Step 2A Prong 2: NO. The claims do not integrate the abstract idea into a practical application.
STEP 28- Inventive Concept
The additional elements are communications circuitry and an image analysis engine. These are generic computer components performing conventional functions (receive data, execute model, output result). Receiving images, running a classification model, and outputting results are well-understood, routine, conventional activities. No inventive concept beyond the
abstract idea.
Step 2B: NO
Dependent claims (26-32, 34-40, 42-43, 48) add object detection (26, 34, and 42), semantic segmentation (27, 35, and 43), orthorectification (28 and 36), ovoid depression
feature identification (29 and 37), CNN (30 and 38), satellite imagery (31 and 39), batch processing (32, 40, and 48). None of these integrate the abstract idea into a practical application.
Claims
Result
Basis
25-32 (Method)
Ineligible
Abstract idea (mathematical concepts/mental process); generic computer implementation; field-of-use limitation only
33-40 (Apparatus)
Ineligible
Same abstract idea; communications circuitry and image analysis engine are generic computer components
41-43, 48 (CPP)
Ineligible
Same abstract idea; non-transitory medium with generic software instructions
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 25-43, and 48 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zgonnik et al. (Evidence for natural molecular hydrogen seepage associated with Carolina bays (surficial, ovoid depressions on the Atlantic Coastal Plain, Province of the USA) – hereinafter “Zgonnik”) in view of Haskin et al. (US 20240020968 A1 – hereinafter “Haskin”).
Claims 25, 33, and 41. (Original)
An apparatus for automatically identifying surface features of the Earth consistent with subsurface hydrogen accumulation (Zgonnik p. 2, Fig. 1 shows a satellite image from Google Earth & left column, “we examined the chemical compositions of the gases from the soils in selected Carolina bays and their vicinities to investigate the contents of H2 … there may be a causal link between these H2 emissions and the formation of these structures”), the apparatus comprising:
communications circuitry configured to receive (Zgonnik p. 6, left column, “LiDAR images were downloaded from the site cintos.org.”) a target image (Zgonnik p. 6, left column, “We identified the bays using satellite images from Google Earth and Google Earth Pro.”); and
(Zgonnik p. 7, right column, discloses locations of high concentration of H2 detected in the bay-like features.”; p. 11, left column, “the causal link between the origin of these morphological features and their association with molecular hydrogen must be established”; p. 12, right column, discloses “This suggests that H2-emitting features are genetically related to structural features of the crystalline basement … potential alignment of the bays along structural trends, suggests a close relationship between Carolina bays and the observed molecular hydrogen seepages from these potential geological structures. The elliptical shape of Carolina bays is the feature that most significantly distinguishes them from the hydrogen seeping structures in the EEC, which usually have rounded shapes.”), wherein the communications circuitry is further configured to (p. 2, Fig. 1 discloses “This and all other satellite images in this article were downloaded from Google Earth and Google Earth Pro,” Downloading images requires communication circuitry to interface with the internet and receive the target image data.) output an indication of whether the target image contains any surface features consistent with subsurface hydrogen accumulation (Zgonnik p. 12, right column & p. 13, right column show that specific surface features are consistent with subsurface hydrogen accumulation; p. 7, right column through p. 9, right column demonstrates the physical mapping of hydrogen to the visual features; Fig’s 1-2, 4, and 7 exemplify the target images that show ovoid surficial depressions acting as visual indicators).
Zgonnik discloses all of the subject matter as described above except for specifically teaching “an image analysis engine configured to identify, using a trained image classification model.” However, Haskin in the same field of endeavor teaches an image analysis engine configured to identify, using a trained image classification model (Haskin ¶130 discloses “classifying a specific object in an image … Typical image classifiers use to carry out the task of detecting an object by scanning the entire image to locate the object”; ¶153 discloses “labeled training data … supervised pre-training”; ¶¶122, 136 discloses “Multi-Layer Perceptron (MLP) when is trained with a supervised learning algorithm”).
Therefore, it would have been obvious to one of ordinary skill in the art to modify the satellite image analysis of Zgonnik by implementing automated machine learning framework taught by Haskin before the effective filing date of the claimed invention. The motivation for this combination of references would have been to apply Haskin’s automated object detection framework to Zgonnik’s manual exploration method in order to rapidly, reliably, and efficiently screen large areas of the Earth for hydrogen-indicating geological features. This constitutes simple automation of a known manual process using well-known machine learning techniques to yield predictable results.
Claims 26, 34, and 42. (Original)
The combination of Zgonnik and Haskin discloses the apparatus of claim 33, wherein the trained image classification model comprises an object detection model, wherein the image analysis engine is configured to identify whether the target image contains surface features consistent with subsurface hydrogen accumulation by identifying any regions within the target image containing surface features consistent with subsurface hydrogen accumulation (Haskin ¶151 discloses “SVM to classify the presence of the object within that candidate region proposal”; ¶152 discloses “For each ROI's output features, a collection of support-vector machine classifiers is used to determine what type of object (if any) is contained within the ROI.”), and wherein the communications circuitry is configured to output any regions (Haskin ¶146 “the output is the class prediction of the object enclosed in the bounding box.”; ¶152 “The original goal of R-CNN was to take an input image and produce a set of bounding boxes as output”; Haskin teaches communication circuitry Fig. 1, 15 and Fig. 4, 44 and 46 to transmit and output data to a remote computer device (¶¶7, 240, 306).) within the target image containing surface features consistent with subsurface hydrogen accumulation (Zgonnik p. 12, right column & p. 13, right column show that specific surface features are consistent with subsurface hydrogen accumulation; p. 7, right column through p. 9, right column demonstrates the physical mapping of hydrogen to the visual features; Fig’s 1-2, 4, and 7 exemplify the target images that show ovoid surficial depressions acting as visual indicators).
The combination of Zgonnik and Haskin renders claim(s) 26, 34, and 42 obvious for the reasons discussed above for claim 25, 33 and 41, mutatis mutandis.
Claims 27, 35, and 43. (Currently amended)
The combination of Zgonnik and Haskin discloses the apparatus of claim 33, wherein the trained image classification model comprises a semantic segmentation model (Haskin ¶177 “Convolutional networks … pixels-to-pixels, exceed the state-of-the-art in semantic segmentation”; ¶153), wherein the image analysis engine is configured to identify whether the target image contains surface features consistent with subsurface hydrogen accumulation (Zgonnik p. 12, right column & p. 13, right column show that specific surface features are consistent with subsurface hydrogen accumulation; p. 7, right column through p. 9, right column demonstrates the physical mapping of hydrogen to the visual features; Fig’s 1-2, 4, and 7 exemplify the target images that show ovoid surficial depressions acting as visual indicators) by identifying a set of pixels in the target image that corresponds to a surface feature consistent with subsurface hydrogen accumulation (Haskin ¶¶ 177-178 “pixels-to-pixels … to produce accurate and detailed segmentations … graph embedding tasks have
a natural correspondence with image pixelwise prediction tasks such as segmentation.”), and wherein the communications circuitry (Haskin teaches communication circuitry Fig. 1, 15 and Fig. 4, 44 and 46 to transmit and output data to a remote computer device (¶¶7, 240, 306).) is configured to output an indication of the set of pixels in the target image (Haskin’s ¶179 disclose U-Net architecture for image segmentation where “the segmentation map only contains the pixels, for which the full context is available in the input image.”) that corresponds to a surface feature consistent with subsurface hydrogen accumulation (Zgonnik p. 12, right column & p. 13, right column show that specific surface features are consistent with subsurface hydrogen accumulation; p. 7, right column through p. 9, right column demonstrates the physical mapping of hydrogen to the visual features; Fig’s 1-2, 4, and 7 exemplify the target images that show ovoid surficial depressions acting as visual indicators).
The combination of Zgonnik and Haskin renders claim(s) 27, 35, and 43 obvious for the reasons discussed above for claim 25, 33 and 41, mutatis mutandis.
Claims 28 and 36. (Currently amended)
The combination of Zgonnik and Haskin discloses the apparatus of claim 33, wherein the image analysis engine is further configured to perform orthorectification on the target image to remove distortion prior to identifying whether the target image contains any surface features consistent with subsurface hydrogen accumulation (Haskin ¶263 discloses an oblique optical image of an area is captures, a “one warped georeferenced orthoimage” (and digital elevation data) is provided, an image warping matrix is created and applied to correct the spatial perspective, and finally “features in the oblique optical image are matched with features in the at least one warped georeferenced orthoimage.” Where, Zgonnik p. 12, right column & p. 13, right column show that specific surface features are consistent with subsurface hydrogen accumulation; p. 7, right column through p. 9, right column demonstrates the physical mapping of hydrogen to the visual features; Fig’s 1-2, 4, and 7).
The combination of Zgonnik and Haskin renders claim(s) 28 and 36 obvious for the reasons discussed above for claim 25, 33 and 41, mutatis mutandis.
Claims 29 and 37. (Currently amended)
The combination of Zgonnik and Haskin discloses the apparatus of claim 33, wherein the surface features consistent with subsurface hydrogen accumulation comprise ovoid surficial depressions (Zgonnik’s title discloses “surficial, ovoid depressions on the Atlantic Coastal Plain, Province of the USA”) or led to the creation of ovoid surficial depressions (Zgonnik p. 2, Fig. 1 shows a satellite image from Google Earth & left column, “we examined the chemical compositions of the gases from the soils in selected Carolina bays and their vicinities to investigate the contents of H2 … there may be a causal link between these H2 emissions and the formation of these structures”).
The combination of Zgonnik and Haskin renders claim(s) 29 and 37 obvious for the reasons discussed above for claim 25, 33 and 41, mutatis mutandis.
Claims 30 and 38. (Currently amended)
The combination of Zgonnik and Haskin discloses the apparatus of claim 33, wherein the trained image classification model comprises a convolutional neural network (Haskin ¶153 “convolutional neural networks (CNNs) … labeled training data”).
The combination of Zgonnik and Haskin renders claim(s) 30 and 38 obvious for the reasons discussed above for claim 25, 33 and 41, mutatis mutandis.
Claims 31 and 39. (Currently amended)
The combination of Zgonnik and Haskin discloses the apparatus of claim 33, wherein the target image comprises: a panchromatic, multispectral, or hyperspectral image; a satellite image (Zgonnik p. 6, left column, “We identified the bays using satellite images from Google Earth and Google Earth Pro.”); or a panchromatic, multispectral or hyperspectral satellite image.
The combination of Zgonnik and Haskin renders claim(s) 31 and 39 obvious for the reasons discussed above for claim 25, 33 and 41, mutatis mutandis.
Claims 32, 40, and 48. (Currently amended)
The combination of Zgonnik and Haskin discloses the apparatus of The apparatus of wherein the communications circuitry is configured to receive a set of target images, wherein the image analysis engine is configured to identify, using the trained image classification model, every target image from the set of target images (Haskin ¶¶ 240 and 243 describes a camera device mounted to a UAV that captures and exchanges “video and other data” and ¶306 “sending the tagged frame to a computer device … over the wireless network.”) that contains any surface features consistent with subsurface hydrogen accumulation (Zgonnik p. 12, right column & p. 13, right column show that specific surface features are consistent with subsurface hydrogen accumulation; p. 7, right column through p. 9, right column demonstrates the physical mapping of hydrogen to the visual features; Fig’s 1-2, 4, and 7), and wherein the communications circuitry is further configured to output an indication of every target image from the set of target images (Haskin ¶306 “sending the tagged frame to a computer device … over the wireless network.” Where, ¶275 discloses tagging the frame based on the neural network’s identification of the object.) that contains surface features consistent with subsurface hydrogen accumulation (Zgonnik p. 12, right column & p. 13, right column show that specific surface features are consistent with subsurface hydrogen accumulation; p. 7, right column through p. 9, right column demonstrates the physical mapping of hydrogen to the visual features; Fig’s 1-2, 4, and 7).
The combination of Zgonnik and Haskin renders claim(s) 32, 40, and 48 obvious for the reasons discussed above for claim 25, 33 and 41, mutatis mutandis. Additionally, it would have been obvious to a person of ordinary skill in the art to apply the batch processing and frame-tagging outputs of Haskin to the method of Zgonnik in order to efficiently filter a large set of imagery and output the specific images containing the hydrogen-indicating surface features for further review.
Conclusion
The prior art made of record but not relied, yet considered pertinent to the applicant’s disclosure, is listed on the PTO-892 form.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ross Varndell whose telephone number is (571)270-1922. The examiner can normally be reached M-F, 9-5 EST.
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/Ross Varndell/Primary Examiner, Art Unit 2674