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 .
Election/Restrictions
Applicant’s arguments, see Response to Election/Restriction, filed 07/07/2025, with respect to Requirement for Restriction/Election have been fully considered and are persuasive. The Requirement for Restriction/Election of claims 1-20 has been withdrawn.
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) 1-9, 11-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sandhu et al. (US2016/0030000) in view of Mansour et al. (US2021/0241428).
To claim 1, Sandhu teach a method comprising:
determining, using a physics-based model (paragraph 0047, acoustomechanical parameter model) and based on a plurality of observations, first solution data, the first solution data descriptive of a first estimated solution to an inverse problem associated with the plurality of observations, wherein the first solution data includes artifacts due, at least in part, to a count of observations of the plurality of observations (paragraph 0047, iterative refinement of the initial model is performed based upon a computed measurement mismatch obtained upon generating a solution to an inverse problem; wherein by definition an inverse problem is the process of finding the causes or parameters that produced a set of observed results, which means a plurality of observations is inherent; paragraphs 0025, 0037, 0044, 0068, occurrence of artifacts is due to a count of observations of said plurality of observations would be obvious); and
performing a plurality of iterations of a gradient descent artifact reduction process to generate second solution data (paragraph 0047, iteratively updated according to a gradient descent method), wherein the artifacts are reduced in the second solution data relative to the first solution data (paragraph 0037, initial model is iteratively refined until artifact reduction satifsfies an artifact threshold condition), and wherein a particular iteration of the gradient descent artifact reduction process (Figs. 1, 6) includes:
determining, a value of a gradient metric associated with particular solution data; and adjusting the particular solution data based on the value of the gradient metric to generate updated solution data (paragraphs 0047-0070, updated model includes generating the updated model based upon a solution to an inverse problem generated with the first simulated wavefield, wherein determining the solution to the inverse problem includes performing an iterative process that processes the initial model with a gradient of an error cost function, and wherein the gradient of the error cost function includes a component derived from the first simulated wavefield).
But, Sandhu do not expressly disclose using a machine-learning model.
Mansour teach using deep learning method to solve inverse problems (paragraphs 0001, 0009, 0051, 0054, 0115, iterates between computing a gradient step, thresholding using a shrinkage operator, projecting to a constraint set, updating a stepsize and updating a solution estimate according to the stepsize. After every iteration the error is reduced. The algorithm iterates until stopping criteria are satisfied).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teaching of Mansour into the method of Sandhu, in order to provide artificial intelligence in effectively solving inverse problem.
To claim 13, Sandhu and Mansour teach a system (as explained in response to claim 1 above).
To claim 20, Sandhu and Mansour teach a computer-readable storage device storing instructions that, when executed by one or more processors (as explained in response to claim 1 above).
To claims 2 and 14, Sandhu and Mansour teach claims 1 and 13.
Though Sandhu and Mansour do not expressly disclose wherein determining the first solution data using the physics-based model comprises performing reverse time migration based on at least a subset of the plurality of observations, reverse time migration algorithm is a well-known practice in the art, which would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate into the method of Sandhu and Mansour to further implementation detail by design preference, hence Official Notice is taken.
To claim 3, Sandhu and Mansour teach claim 1.
Sandhu and Mansour teach wherein the plurality of observations comprises waveform return data (Sandhu, paragraph 0004, scattered wave return data).
To claims 4 and 15, Sandhu and Mansour teach claims 3 and 13.
Sandhu and Mansour teach wherein the waveform return data includes seismic returns, acoustic returns, or electromagnetic returns (Sandhu, paragraph 0004, acoustic returns).
To claim 5, Sandhu and Mansour teach claim 1.
Sandhu and Mansour teach wherein the first estimated solution to the inverse problem comprises a reflectivity image (Sandhu, paragraph 0004, ultrasound image reconstruction).
To claims 6 and 16, Sandhu and Mansour teach claims 1 and 13.
Sandhu and Mansour teach further comprising, after determining the second solution data, providing the second solution data as input to the physics-based model to generate third solution data (obvious in iteration operations, such as Fig. 6 of Sandhu, wherein updated solution is fed back as input).
To claims 7 and 17, Sandhu and Mansour teach claims 6 and 16.
Sandhu and Mansour teach further comprising performing a second plurality of iterations of the gradient descent artifact reduction process to generate fourth solution data, wherein the artifacts are reduced in the fourth solution data relative to the third solution data (obvious in iteration operations, such as Fig. 6 of Sandhu, wherein updated solution is fed back as input).
To claim 8, Sandhu and Mansour teach claim 1.
Sandhu and Mansour teach wherein, during the particular iteration, the particular solution data is adjusted further based on a step size parameter (Sandhu, paragraph 0053).
To claims 9 and 18, Sandhu and Mansour teach claims 8 and 13.
Sandhu and Mansour teach further comprising, after performing the plurality of iterations: adjusting the step size parameter; and performing a second plurality of iterations (Sandhu, paragraph 0053).
To claims 11 and 19, Sandhu and Mansour teach claims 1 and 13.
Sandhu and Mansour teach wherein, during the particular iteration, the particular solution data is adjusted further based on a regularization term that is based on total variation of solution data of the plurality of iterations of the gradient descent artifact reduction process (obvious in Mansour, paragraph 0095, total variation norm).
To claim 12, Sandhu and Mansour teach claim 1.
Sandhu and Mansour teach wherein the machine-learning model corresponds to a score-matching network (using a particular type of deep learning model would be obvious in view of Sandhu, paragraphs 0030, 0044-0045, and Mansour, paragraphs 0051-0052, Official Notice is also taken).
Allowable Subject Matter
Claim 10 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZHIYU LU whose telephone number is (571)272-2837. The examiner can normally be reached Weekdays: 8:30AM - 5:00PM.
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ZHIYU . LU
Primary Examiner
Art Unit 2669
/ZHIYU LU/Primary Examiner, Art Unit 2665 October 16, 2025