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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/30/2025 has been entered.
Status of Claims
Claims 1, 6, 12, 16, 17, 20, 21 and 23-25 have been amended.
Claims 2-5, 22 and 26 have been cancelled.
Claims 7-11, 13-15, 18 and 19 have been previously presented.
Response to Amendment
The amendment filed 11/30/25 is objected to under 35 U.S.C. 132(a) because it introduces new matter into the disclosure. The new matter is underlined in the following 11/30/25 Specification amendment shown below. 35 U.S.C. 132(a) states that no amendment shall introduce new matter into the disclosure of the invention. The added material which is not supported by the original disclosure is as follows:
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Applicant is required to cancel the new matter in the reply to this Office Action.
Claim Rejections - 35 USC § 112
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.
Independent claims 1, 16 and 21, as well as dependent claims 6-15, 17-20 and claims 23-25 incorporated by reference, are rejected under 35 U.S.C. 112(a), as failing to comply with the written description requirement. The claims 1, 6-21 and 23-25 contain 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. The following new subject matter recited in claims 1, 6-21 and 23-25 was not described in of the applicant’s originally filed Specification:
1. A method of illuminating a target located below the Earth’s surface, comprising: deploying one or more physical sensors above the surface of the Earth; obtaining data from the one or more physical sensors; transforming the data from the one or more physical sensors to a one-dimensional vector via an adjoint operator; processing the one-dimensional vector with a computer system to obtain a 3D image reconstruction of the target.
6. The method of claim 1, wherein the computer system includes machine learning algorithms for unstructured meshes.
7. The method of claim 1, wherein the target is unexploded ordinance.
8. The method of claim 1, wherein the target is a subsurface pipeline.
9. The method of claim 1, wherein the resulting multidimensional image is suitable for use in mining.
10. The method of claim 1, wherein the resulting multidimensional image is suitable for use in agriculture.
11. The method of claim 1, wherein the resulting multidimensional image is used to detect pipeline integrity.
12. The method of claim 6, wherein the machine learning algorithms for unstructured meshes are stored on a non-transitory memory that is configured to receive the data from the one or more physical sensors.
13. The method of claim 1, wherein the one or more physical sensors are geophysical sensors configured to illuminate the target using an active transmitter source comprising electromagnetic radiation.
14. The method of claim 1, wherein the one or more physical sensors are geophysical sensors configured to illuminate the target using an active acoustic transmission source.
15. The method of claim 1, wherein the one or more physical sensors are passive geophysical sensors configured to detect gravitational attraction and its gradients.
16. A method of illuminating a target, comprising: deploying one or more physical sensors to a location on or above the Earth’s surface; obtaining a plurality of data sets from the one or more physical sensors; transforming a first data set of the plurality of data sets to a first one-dimensional vector via an adjoint operator; transforming the first one-dimensional vector to a second one-dimensional vector via a first set of mathematical operators; transforming the second one-dimensional vector to a third one-dimensional vector via a second set of mathematical operators; processing the third one-dimensional vector with a computer system to obtain a 3D image reconstruction of the target.
17. The method of claim 16, wherein the computer system includes machine learning algorithms for unstructured meshes, and wherein the transformations of the first data set, the first one-dimensional vector, and the second one-dimensional vector are performed via the computer system.
18. The method of claim 16, wherein the one or more physical sensors are geophysical sensors configured to illuminate the target using an active transmitter source comprising electromagnetic radiation.
19. The method of claim 16, wherein the one or more physical sensors are geophysical sensors configured to illuminate the target using an active acoustic transmission source.
20. The method of claim 16, wherein the computer system comprises a computer model stored on a non-transitory memory that is configured to receive the data from the one or more physical sensors.
21. A method of illuminating a target, comprising: deploying one or more geophysical sensors to a location on or above a surface, wherein the geophysical sensors are configured to illuminate the target using an active transmitter source comprising electromagnetic radiation; obtaining a plurality of data sets from the one or more geophysical sensors; transforming a first data set of the plurality of data sets to a first one-dimensional vector via an adjoint operator; transforming the first one-dimensional vector to a second one-dimensional vector via a first set of mathematical operators; transforming the second one-dimensional vector to a third one-dimensional vector via a second set of mathematical operators; processing the third one-dimensional vector with a computer system comprising a computer model stored on a non-transitory memory configured to receive the data from the one or more geophysical sensors to obtain a 3D image reconstruction of the target, wherein the computer model comprises machine learning algorithms for unstructured meshes, and wherein the transformations of the first data set, the first one-dimensional vector, and the second one-dimensional vector are performed via the computer system.
23. The method of claim 21, wherein the surface is the Earth’s surface and the target is a subsurface pipeline.
24. The method of claim 21, wherein the surface is the Earth’s surface and the resulting 3D image is suitable for use in mining for detection of minerals below the Earth’s surface.
25. The method of claim 21, wherein the surface is the Earth’s surface and the resulting 3D image is suitable for use in agricultural applications.
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.
Claims 1, 6-21 and 23-25 are rejected under 35 U.S.C. 103 as being unpatentable over Haddad (US 2011/0202277) in view of Zhdanov et al.(US Patent Pub. 2013/0018585).
Regarding claim 1, Haddad teaches a method of illuminating a target located below the Earth’s surface (0005 lines 1-6), comprising:
deploying one or more physical sensors above the surface of the Earth (0005 lines 1-6);
obtaining data from the one or more physical sensors (0010 lines 1-18);
transforming the data from the one or more physical sensors to a one-dimensional vector via an operator (0105 lines 9-14 and 0115 lines 1-17);
processing the one-dimensional vector with a computer system to obtain a 3D image reconstruction of the target (0115 lines 1-17). However, Haddad fails to teach an adjoint operator. Zhdanov teaches an adjoint operator (0090 lines 1-11). Therefore it would have been obvious to one of ordinary skill in the art to combine the subsurface detection techniques of Haddad with the adjoint operators of Zhdanov because this combination would improve the computational accuracy of complex subsurface senor data through utilization of adjoint operators within dimensional data calculations.
Regarding claim 6, Haddad teaches wherein the computer system includes machine learning algorithms (0009 lines 1-17) for unstructured meshes (Fig. 8).
Regarding claim 7, Haddad teaches wherein the target is unexploded ordinance (0103 lines 1-15).
Regarding claim 8 and 23, Haddad teaches wherein the target is a subsurface pipeline (0103 lines 1-15).
Regarding claim 9 and 24, Haddad teaches wherein the resulting multidimensional image is suitable for use in mining (0101 lines 1-14).
Regarding claim 10 and 25, Haddad teaches wherein the resulting multidimensional image is suitable for use in agriculture (0093 lines 1-18).
Regarding claim 11, Haddad teaches wherein the resulting multidimensional image is used to detect pipeline integrity (0101 lines 1-16).
Regarding claim 12 and 20, Haddad teaches wherein the machine learning algorithms for unstructured meshes are stored on a non-transitory memory that is configured to receive the data from the one or more physical sensors (0009 lines 1-17).
Regarding claim 13 and 18, Haddad teaches wherein the one or more physical sensors are geophysical sensors configured to illuminate the target using an active transmitter source comprising electromagnetic radiation (Fig. 17).
Regarding claim 14 and 19, Haddad teaches wherein the one or more physical sensors are geophysical sensors configured to illuminate the target using an active acoustic transmission source (0036 lines 1-19).
Regarding claim 15, Haddad teaches wherein the one or more physical sensors are passive geophysical sensors configured to detect gravitational attraction and its gradients (0075 lines 1-13).
Regarding claim 16, Haddad teaches a method of illuminating a target (0005 lines 1-6), comprising:
deploying one or more physical sensors to a location on or above the Earth's surface (0005 lines 1-6);
obtaining a plurality of data sets from the one or more physical sensors (0010 lines 1-18);
transforming a first data set of the plurality of data sets to a first one-dimensional vector (0105 lines 9-14 and 0115 lines 1-17);
transforming the first one-dimensional vector to a second one-dimensional vector via an operator (0105 lines 9-14 and 0115 lines 1-17, in which a plurality of data sets are converted, therefore any subsequent or second data set would be transformed);
transforming the second one-dimensional vector to a third one-dimensional vector via a second set of mathematical operators (0105 lines 9-14 and 0115 lines 1-17, in which a plurality of data sets are converted, therefore any subsequent or third data set would be transformed);
processing the third one-dimensional vector with a computer system to obtain a 3D image reconstruction of the target (0115 lines 1-17). However, Haddad fails to teach an adjoint operator. Zhdanov teaches an adjoint operator (0090 lines 1-11). Therefore it would have been obvious to one of ordinary skill in the art to combine the subsurface detection techniques of Haddad with the adjoint operators of Zhdanov because this combination would improve the computational accuracy of complex subsurface senor data through utilization of adjoint operators within dimensional data calculations.
Regarding claim 17, Haddad teaches wherein the computer system includes machine learning algorithms (0009 lines 1-17) for unstructured meshes (Fig. 8), and wherein the transformations of the first data set, the first one-dimensional vector, and the second one-dimensional vector are performed via the computer system (0005 lines 1-6).
Regarding claim 21, Haddad teaches a method of illuminating a target (0005 lines 1-6), comprising:
deploying one or more geophysical sensors to a location on or above a surface, wherein the geophysical sensors are configured to illuminate the target using an active transmitter source comprising electromagnetic radiation (Fig. 17);
obtaining a plurality of data sets from the one or more geophysical sensors (0010 lines 1-18);
transforming a first data set of the plurality of data sets to a first one-dimensional vector via an operator (0105 lines 9-14 and 0115 lines 1-17);
transforming the first one-dimensional vector to a second one-dimensional vector via a first set of mathematical operators (0105 lines 9-14 and 0115 lines 1-17, in which a plurality of data sets are converted, therefore any subsequent or second data set would be transformed);
transforming the second one-dimensional vector to a third one-dimensional vector via a second set of mathematical operators (0105 lines 9-14 and 0115 lines 1-17, in which a plurality of data sets are converted, therefore any subsequent or third data set would be transformed);
processing the third one-dimensional vector with a computer system comprising a computer model (0009 lines 1-17) stored on a non-transitory memory configured to receive the data from the one or more geophysical sensors to obtain a 3D image reconstruction of the target (0009 lines 1-17), wherein the transformations of the first data set, the first one-dimensional vector, and the second one-dimensional vector are performed via the computer system (0005 lines 1-6). However, Haddad fails to teach an adjoint operator. Zhdanov teaches an adjoint operator (0090 lines 1-11). Therefore it would have been obvious to one of ordinary skill in the art to combine the subsurface detection techniques of Haddad with the adjoint operators of Zhdanov because this combination would improve the computational accuracy of complex subsurface senor data through utilization of adjoint operators within dimensional data calculations.
Response to Arguments
Applicant’s arguments filed 11/30/25 have been fully considered but they are not persuasive.
The applicant argues that the amendments to the Specification filed on 11/30/25 are fully supported by the original disclosure. However, the amendments to the Specification change the scope of the invention recited in the Specification. Therefore, the new matter rejection of the 11/30/25 Specification amendment has been provided in this office action and the applicant is required to cancel the new matter in the reply to this office action.
The claim objection to claims 19-24 has been withdrawn in view of the 11/30/25 claim amendments to correct claims 23-25 as suggested in the previous office action. The 35 U.S.C. 112(a) rejection of claims 1 and 6-26 for failing to comply with the written description requirement has been maintained because the Specification fails to recite support for the claimed subject matter recited in the 11/30/25 amendments to claims 1, 6-21 and 23-25.
The applicants argue that Haddad and Zhdanov fail to mention or suggest the use of machine learning algorithms as recited in claims 6, 12, 17 and 21. However, Haddad clearly teaches algorithms that change with respect to the detection of derivative images (0051 lines 1-11) thereby providing a computer implemented algorithm that adaptively learns as commonly know in the art. Therefore the 35 U.S.C. 103 rejection of claims 6, 12, 17 and 21 has been maintained in view of the teachings of Haddad.
Conclusion
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