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 .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-3, 5, 9-12 and 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Maximo (US 20220327713) in view of Kirillov et al. "Segment anything." Proceedings of the IEEE/CVF international conference on computer vision. 2023.
As to claim 1, Maximo discloses a system (FIG. 1), comprising:
one or more processors (see [0039]); and
memory, accessible by the one or more processors, and storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations (see [0039]) comprising:
receiving image data of an image of rock samples from an imaging system (see [0063]);
analyzing the image data via an Artificial Intelligence (AI) model (see [0054], [0067]); and
generating a segmented image based on the analyzing of the image data via the AI model (see [0066]-[0067]), wherein the segmented image comprises characterizations of the rock samples of the image data (see [0049], [0073]).
Maximo fails to explicitly disclose that the Artificial Intelligence (AI) model is trained using zero-shot learning in which the AI model is trained without any rock particle specific training and is instead trained using alternative images in place of rock particle images.
However, Kirillov teaches training an Artificial Intelligence (AI) model using zero-shot learning in which the AI model is trained without any rock particle specific training and is instead trained using alternative images in place of rock particle images (see FIG. 1, Section 2 and Section 3: Segment Anything Model (SAM) that powers data annotation and enables zero-shot transfer to a range of tasks via prompt engineering, and a data engine for collecting SA-1B, our dataset of over 1 billion masks).
At the time before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skills in the art to modify Maximo using Kirillov’s teachings to train the Artificial Intelligence (AI) model using zero-shot learning in which the AI model is trained without any rock particle specific training and is instead trained using alternative images in place of rock particle images in order to power new capabilities even beyond ones imagined at the moment of training and allow the system to work without need for additional training (Kirillov; Section 7).
As to claim 2, modified Maximo further discloses wherein analyzing the image data further comprises applying a first level of resolution to the image data when generating the segmented image (see [0047]).
As to claim 3, modified Maximo further discloses wherein the first level of resolution corresponds to a default resolution setting (see [0047]).
As to claim 5, modified Maximo further discloses wherein analyzing the image data further comprises applying a second level of resolution to the image data in place of the first level of resolution when generating the segmented image (see [0047]).
As to claim 9, modified Maximo further discloses further comprising:
analyzing the segmented image via the AI model (see [0057]); and
generating a second segmented image based on the analyzing of the segmented image via the AI model, wherein the second segmented image comprises second characterizations of the rock samples of the image data (see [0060]).
As to claim 10, modified Maximo further discloses wherein analyzing the segmented image further comprises applying a second level of resolution to the segmented image to generate the second segmented image (see [0047]).
As to claim 11, Maximo discloses a computer-implemented method, comprising:
receiving image data of an image of rock samples from an imaging system (see [0063]);
analyzing the image data via an Artificial Intelligence (AI) model (see [0054], [0067]); and
generating a segmented image based on the analyzing of the image data via the AI model (see [0066]-[0067]), wherein the segmented image comprises characterizations of the rock samples of the image data (see [0049], [0073]).
Maximo fails to explicitly disclose that the Artificial Intelligence (AI) model is trained using zero-shot learning in which the AI model is trained without any rock particle specific training and is instead trained using alternative images in place of rock particle images; wherein the AI model comprises a Large Foundation Model that generates the segmented image comprising characterizations of the rock samples of the image data via classification of the image data at a pixel level whereby each pixel of the image data is classified.
However, Kirillov teaches training an Artificial Intelligence (AI) model using zero-shot learning in which the AI model is trained without any rock particle specific training and is instead trained using alternative images in place of rock particle images (see FIG. 1, Section 2 and Section 3: Segment Anything Model (SAM) that powers data annotation and enables zero-shot transfer to a range of tasks via prompt engineering, and a data engine for collecting SA-1B, our dataset of over 1 billion masks);
wherein the AI model comprises a Large Foundation Model that generates the segmented image comprising characterizations of the rock samples of the image data via classification of the image data at a pixel level whereby each pixel of the image data is classified (FIG. 4 and Section 3: Segment Anything Model (SAM) which is a Large Foundation Model that classify images at pixel level).
At the time before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skills in the art to modify Maximo using Kirillov’s teachings to train the Artificial Intelligence (AI) model using zero-shot learning in which the AI model is trained without any rock particle specific training and is instead trained using alternative images in place of rock particle images, wherein the AI model comprises a Large Foundation Model that generates the segmented image comprising characterizations of the rock samples of the image data via classification of the image data at a pixel level whereby each pixel of the image data is classified in order to power new capabilities even beyond ones imagined at the moment of training and allow the system to work without need for additional training (Kirillov; Section 7).
As to claims 12 and 16-17, method claims 12 and 16-17 recite the same features as those recited in claims 2 and 9-10, respectively, and are therefore rejected for the same reasons as above.
As to claim 18, Maximo discloses a device (FIG. 3, FIG. 8), comprising:
a display (see [0039], [0050], [0081]);
a processor communicatively coupled to the display (see [0039]); and
a memory communicatively coupled to the processor, the memory storing instructions which, when executed, cause the processor to perform operations (see [0039]) comprising:
generating a graphical user interface (GUI) (FIG. 3, GUI 314);
generating an image of rock samples corresponding to image data received from an imaging system for presentation on the display (FIG. 2A, image 200; see [0063]);
receiving a first input via a first user interaction with the GUI (FIG. 2A and [0034]);
generating a visual icon for display on the display at a particular location on the image of rock samples displayed on the display, wherein the particular location is determined based upon the first input (FIG. 2A, icons 202, 204, 206; see [0034]-[0035]);
receiving a second input via a second user interaction with the GUI (see [0036], the regions 222a-d, which may be identified based on the user inputs 202a-d … the region 226, which may be identified based on the user input 206; see [0037], a user input for segmentation of an image may additionally or alternatively indicate an outline of an area corresponding to a particular channel); and
in response to the second input, analyzing at least a portion of the image data via an Artificial Intelligence (AI) model and generating a segmented image based on the analyzing of the at least a portion of the image data by the AI model (FIG. 2B and [0036]-[0037], segmented image 220), wherein the segmented image comprises characterizations of the at least a portion of the rock samples (see [0049], [0073]).
Maximo fails to explicitly disclose that the Artificial Intelligence (AI) model is trained using zero-shot learning in which the AI model is trained without any rock particle specific training and is instead trained using alternative images in place of rock particle images.
However, Kirillov teaches training an Artificial Intelligence (AI) model using zero-shot learning in which the AI model is trained without any rock particle specific training and is instead trained using alternative images in place of rock particle images (see FIG. 1, Section 2 and Section 3: Segment Anything Model (SAM) that powers data annotation and enables zero-shot transfer to a range of tasks via prompt engineering, and a data engine for collecting SA-1B, our dataset of over 1 billion masks);
At the time before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skills in the art to modify Maximo using Kirillov’s teachings to train the Artificial Intelligence (AI) model using zero-shot learning in which the AI model is trained without any rock particle specific training and is instead trained using alternative images in place of rock particle images in order to power new capabilities even beyond ones imagined at the moment of training and allow the system to work without need for additional training (Kirillov; Section 7).
As to claim 19, modified Maximo further discloses wherein the at least a portion of the image data analyzed by the AI model corresponds to the particular location on the image of rock samples (FIGS. 2A-2B).
As to claim 20, modified Maximo discloses further comprising:
receiving a third input via a third user interaction with the GUI (FIG. 2A and [0034]); and
generating a second visual icon for display on the display at a second particular location on the image of rock samples displayed on the display, wherein the second particular location is determined based upon the second input (FIG. 2A, icons 202, 204, 206; see [0034]-[0035]).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Maximo (US 20220327713) in view of Kirillov et al. "Segment anything." Proceedings of the IEEE/CVF international conference on computer vision. 2023. in view of WOLDEAMANUEL et al (US 20240280461).
As to claim 4, The combination of Maximo and Kirillov fails to explicitly disclose wherein the first level of resolution corresponds to a user selected resolution setting.
However, WOLDEAMANUEL teaches wherein the first level of resolution corresponds to a user selected resolution setting (see [0019]).
At the time before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skills in the art to modify Maximo using WOLDEAMANUEL’s teachings to include wherein the first level of resolution corresponds to a user selected resolution setting in order to improve identification of secondary porosities (WOLDEAMANUEL; [0014]).
Claim(s) 6-8 and 13-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Maximo (US 20220327713) in view of Kirillov et al. "Segment anything." Proceedings of the IEEE/CVF international conference on computer vision. 2023. in view of Agarwal et al (US 20240144458).
As to claim 6, the combination of Maximo and Kirillov further discloses further comprising:
receiving a user input corresponding to a first area of the segmented image (Maximo; see [0051], GUI 314 enables a user 340 to view and/or interact directly with the characterization of the reservoir rock sample … a user input may be provided to modify, accept, or reject the sample segmentation data 336 … the sample segmentation data 336 may thus be updated based on a user input); and
generating an adjusted segmented image (Maximo; see [0051], a user input may be provided to modify, accept, or reject the sample segmentation data 336 … the sample segmentation data 336 may thus be updated based on a user input).
The combination of Maximo and Kirillov fails to explicitly discloses that the adjusted segmented image is generated by removing the rock samples associated with the first area of segmented image from the segmented image.
However, Agarwal teaches generating an adjusted segmented image by removing the rock samples associated with the first area of segmented image from the segmented image (see [0029]).
At the time before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skills in the art to modify the combination of Maximo and Kirillov using Agarwal’s teachings to generate an adjusted segmented image by removing the rock samples associated with the first area of segmented image from the segmented image in order to optimize drilling operations (Agarwal; [0013]).
As to claim 7, the combination of Maximo, Kirillov and Agarwal further discloses further comprising:
analyzing the adjusted segmented image via the AI model (Maximo; see [0057]); and
generating a second segmented image based on the analyzing of the adjusted segmented image via the AI model, wherein the second segmented image comprises second characterizations of the rock samples of the image data (Maximo; see [0060]).
As to claim 8, the combination of Maximo, Kirillov and Agarwal further discloses wherein analyzing the adjusted segmented image further comprises applying a second level of resolution to the adjusted segmented image to generate the second segmented image (Maximo; see [0047]).
As to claims 13-15, method claims 13-15 recite the same features as those recited in claims 6-8, respectively, and are therefore rejected for the same reasons as above.
Response to Arguments
Applicant’s amendments and arguments, filed on 05/19/2026, with respect to the rejection(s) of claim(s) 1, 11 and 18 under 102 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Kirillov et al. "Segment anything." Proceedings of the IEEE/CVF international conference on computer vision. 2023.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BOUBACAR ABDOU TCHOUSSOU whose telephone number is (571)272-7625. The examiner can normally be reached M-F 8am-4pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chris Kelley can be reached at 5712727331. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BOUBACAR ABDOU TCHOUSSOU/Primary Examiner, Art Unit 2482
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