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 .
Status of the claims
The argument received on July 17, 2026 has been acknowledged and entered. Claims 1, 18, and 23 are amended. Claims 3-5, 7-9, 11, 13, 22, 25-27, 29-31, 33, 35, and 40-41 are previously cancelled. Thus, claims 1, 2, 6, 10, 12, 14-21, 23-24, 28, 32, 34, and 36-39 are currently pending.
Responses to Arguments
Applicant’s amendments filed July 17, 2026 with respect to the rejection under 35 U.S.C. 101 have been fully considered but are moot because the new ground of rejection. However, since the arguments are related to current rejection, Applicant’s arguments are addressed as follows:
On the pages 10-11 of the Remarks, Applicant alleges that “[A]mended Claim 1 explicitly integrates compressive sensing mathematics into a practical application by claiming a dynamic, multi-stage data-routing architecture that improves the functioning of NDE/SHM systems. The amended claim does not merely recite "apply compressive sensing on a computer….Such affirmatively recited specific routing mechanism is a technologically rooted solution that improves the NDE/SHM system by allowing a single, unified architecture to efficiently process significantly different physical modalities of NDE data (e.g., A-scans VS. C-scans VS. pitch-catch) while actively minimizing unnecessary computational overhead (bypassing stages) and reducing hardware data acquisition burdens. Such specific architectural control scheme goes far beyond insignificant extra-solution activity and directly integrates the disclosed mathematics into a highly specific technical improvement.”
Examiner respectfully disagrees. The claim language in claim 1 here provides only a result-oriented solution with insufficient detail, unlike examples 3 and 21, how a computer accomplishes it. The claims are only describing the results, not how the results (i.e., reducing hardware data acquisition burdens) are achieved. Further, an abstract idea itself is just that, abstract, and whether such feature is or is not significant does not preclude it from being considered abstract. An abstract idea by itself, whether it or not it has a benefit, does not reasonably overcome a 101 rejection because it is still an abstract idea. Therefore, the above advantages relate to abstract idea limitations which are not considered. The Improvements in the abstract idea are not qualified as improvements indicating a practical application. The pending claims are not patent eligible since a claim for a new abstract idea is still an abstract idea (see MPEP 2106.05(a).I) and an improvement in the abstract idea itself is not an improvement in technology (see MPEP 2106.05(a).II and MPEP 2106.05(a).II: Examples that the courts have indicated may not be sufficient to show an improvement to technology include: iii. Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48)). This is just a processor running mathematics and/or mental processes. Similar limitations comprise the abstract ideas of Claim 18 and 23. Therefore, the independent claims 1, 18, and 23 are ineligible.
Applicant’s amendments filed July 17, 2026 with respect to the rejection under 35 U.S.C. 103 have been fully considered but are moot because the new ground of rejection.
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, 2, 6, 10, 12, 14-21, 23-24, 28, 32, 34, and 36-39are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Specifically, representative Claim 1 recites:
Method for generalized compressive sensing process for automated reduction of data fully sampled non-destructive evaluation/structural health monitoring (NDE/SHM) data, for use with different types of NDE/SHM systems, comprising:
obtaining fully sampled data by conducting data acquisition relative to a target structure for non-destructive evaluation/structural health monitoring;
obtaining undersampled data by applying a selected compression ratio to the fully sampled data; and
conducting at least one of storing or transmitting the undersampled data;
determining whether the undersampled data comprises data undersampled in both time and space, data undersampled in time only, or data undersampled in space only;
usinq compressive sensing to reconstruct the fully sampled data from the undersampled data by selectively routing the undersampled data through a multi-stage reconstruction dataflow based on said determining, wherein:
data undersampled in both time and space flows through a first stage for
temporal reconstruction and a second stage for spatial reconstruction,
data undersampled in time only flows through only the first stage for
temporal reconstruction, and
data undersampled in space only bypasses the first stage and flows
directly to the second stage for spatial reconstruction;
analyzinq the reconstructed fully sampled data to detect structural damaqe in the target structure; and
usinq a qraphical user interface (GUI) configured to:
receive the fully sampled data as input;
receive a selection of the compression ratio (CR); and
display a visual comparison of the fully sampled data and the reconstructed fully sampled data to verify accuracy of the reconstruction.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements.”
Step 1: under the Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (Process).
Step 2A, Prong One: under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the groupings of subject matter when recited as such in a claim limitation that falls into the grouping of subject matter when recited as such in a claim limitation, that covers mathematical concepts - mathematical relationships, mathematical formulas or equations, mathematical calculations and mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion.
For example, the limitations of determining whether the undersampled data comprises data undersampled in both time and space, data undersampled in time only, or data undersampled in space only (paras. [0023], [0033] of instant application),” “obtaining undersampled data by applying a selected compression ratio to the fully sampled data (see paras. [00037], [00044], [00049] of instant application)” are mathematical calculations. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation (see MPEP 2016.04(a)(2)C).
“using compressive sensing to reconstruct the fully sampled data from the undersampled data using compressive sensing to reconstruct the fully sampled data from the undersampled data (see paras. [00029]-[00035], [00040], [00044]-[00050] of instant application) by selectively routing the undersampled data through a multi-stage reconstruction dataflow based on said determining, wherein: data undersampled in both time and space flows through a first stage for temporal reconstruction and a second stage for spatial reconstruction, data undersampled in time only flows through only the first stage for temporal reconstruction, and data undersampled in space only bypasses the first stage and flows directly to the second stage for spatial reconstruction (see paras. [00023], [00033], [00050] of instant application)” are mathematical calculations based on mental processes. The limitation of reconstructing the fully sampled data is an indicative of mathematical and the limitation of calculations and selectively routing is mental processes (evaluation/judgement). Further, the limitation of “analyzing the reconstructed fully sampled data to detect structural damage in the target structure (see paras [00029]-[00030], [00036], [00050] of instant application)” is mental process (observation/evaluation). Looking at the data or graphs in Figs. 4a-b and Figs. 5 and making a decision is evaluation/judgement in mental process. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mathematical calculations and/or human mind, then it falls within the “Mathematical Concepts” and/or “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Similar limitations comprise the abstract ideas of Claims 18 and 23.
Step 2A, Prong Two: under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application. In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception. This judicial exception is not integrated into a practical application.
Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
Step 2B:
The above claims comprise the following additional elements:
In Claim 1: method of generalized compressive sensing process for automated reduction of fully sampled non-destructive evaluation/structural health monitoring (NDE/SHM) data, for use with different types of NDE/SHM systems for use with different types of NDE/SHE systems (preamble); a graphical user interface (GUI); steps of obtaining fully sampled data by conducting data acquisition relative to a target structure for non-destructive evaluation/structural health monitoring; conducting at least one of storing or transmitting the undersampled data; steps of receive the fully sampled data as input; receive a selection of the compression ratio (CR) and receive a selection of the compression ratio (CR); and display a visual comparison of the fully sampled data and the reconstructed fully sampled data to verify accuracy of the reconstruction;
In Claim 18: methodology for non-destructive evaluation/structural health monitoring (NDE/SHM) of structures based on pitch-catch ultrasonic guided waves, using a degree of data acquisition from a target structure that is relatively reduced from what is required for a full sampling of the target structure, while maintaining the ability to accurately detect, locate, and characterize damage to the target structure (preamble); a graphical user interface (GUI); obtaining a set of fully sampled sensor signals from actuator-sensor paths of the target structure; inputting the set of fully sampled sensor signals; display a visual comparison of the fully sampled data and the reconstructed fully sampled data to verify accuracy of the reconstruction; and
In Claim 23: a non-destructive evaluation/structural health monitoring (NDE/SHM) system for generalized compressive sensing process for automated reduction of fully sampled non-destructive evaluation/structural health monitoring (NDE/SHM) data, for use with different types of NDE/SHM data (preamble); a graphical user interface (GUI); one or more tangible, non-transitory computer-readable media that collectively store instructions that, when executed, cause a computing device comprising one or more processors to perform operations; steps of obtaining fully sampled data by conducting data acquisition relative to a target structure for nondestructive evaluation/structural health monitoring; conducting at least one of storing or transmitting the undersampled data; steps of receive the fully sampled data as input; receive a selection of the compression ratio (CR) and receive a selection of the compression ratio (CR); and display a visual comparison of the fully sampled data and the reconstructed fully sampled data to verify accuracy of the reconstruction.
The additional elements such as non-destructive evaluation/structural health monitoring (NDE/SHM) system, non-destructive evaluation/structural health monitoring (NDE/SHM) of structures based on pitch-catch ultrasonic, one or more tangible, non-transitory computer-readable media, a graphical user interface (GUI) are recited at a high-level generality without descriptions of its specific structure/features to perform the claimed features for producing the mathematical and mental processes addressed above (MPEP 2106.05(d)). Further, the addition elements of “Method for generalized compressive sensing process for automated reduction of fully sampled non-destructive evaluation/structural health monitoring (NDE/SHM) data, for use with different types of NDE/SHM systems,” “Methodology for non-destructive evaluation/structural health monitoring (NDE/SHM) of structures based on pitch-catch ultrasonic guided waves, using a degree of data acquisition from a target structure that is relatively reduced from what is required for a full sampling of the target structure, while maintaining the ability to accurately detect, locate, and characterize damage to the target structure,” and “A non-destructive evaluation/structural health monitoring (NDE/SHM) system for generalized compressive sensing process for automated reduction of fully sampled non-destructive evaluation/structural health monitoring (NDE/SHM) data, for use with different types of NDE/SHM data” are preamble statements reciting purpose or intended use (See MPEP 2111.02)(II)). Further, the addition elements of “obtaining fully sampled data by conducting data acquisition relative to a target structure for non-destructive evaluation/structural health monitoring,” “receive the fully sampled data as input,” “receive the set of fully sampled sensor signals as input,” and “receive a selection of the compression ratio (CR)” are insignificant extra-solution activity (gathering data) that cannot reasonably integrate the judicial exception into a practical application (see MPEP 2106.05(g)). Further, the additional limitations of “display a visual comparison of the fully sampled data and the reconstructed fully sampled data to verify accuracy of the reconstruction” is insignificant (post-solution) extra-solution activity. That is, receiving data and displaying are insignificant post-solution activity. Merely “displaying ” a result (i.e., visual comparison of the fully sampled data and the reconstructed fully sampled data to perform abstract) is nothing more than outputting a signal or displaying result. There is established case law (electric power group for example) to prove that such a feature is insufficient extra solution activity (see MPEP 2106.05(g)). Furthermore, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because these additional elements/steps are well-understood, routine, and conventional in the relevant based on the prior art of record (Le Duff (WO 2021189130 A1), Cella (US 2020/0201292 A1)). For example, Le Duff and Cella teach conducting at least one of storing or transmitting the undersampled data (para. [0025], [0029] of Le Duff; para. [0198] of Cella) in claim 1.
Claim 1 does not present tangible or physical elements/components and/or integration of improvements to be indicative of specific features/structure/acts, for example, how and or with what to obtaining fully sampled data, obtaining undersampled data, reconstruct the fully sampled data, and analyzing the reconstructed fully sampled data. Therefore, the claim have no significance more beyond the abstract idea. Further, an abstract idea itself is just that, abstract, and whether such feature is or is not significant does not preclude it from being considered abstract. An abstract idea by itself, whether it or not it has a benefit, does not reasonably overcome a 101 rejection because it is still an abstract idea. Therefore, the above advantages relate to abstract idea limitations which are not considered. The Improvements in the abstract idea are not qualified as improvements indicating a practical application. The pending claims are not patent eligible since a claim for a new abstract idea is still an abstract idea (see MPEP 2106.05(a).I) and an improvement in the abstract idea itself is not an improvement in technology (see MPEP 2106.05(a).II and MPEP 2106.05(a).II: Examples that the courts have indicated may not be sufficient to show an improvement to technology include: iii. Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48)). This is just a processor running mathematics and/or mental processes. Similar limitations comprise the abstract ideas of Claim 18 and 23. Therefore, the independent claims 1, 18, and 23 are ineligible.
Regarding claims 2, 6, 10, 12, 14-17, 20-21, 24, 28, 34, and 36-39,
All features recited in these claims are abstract ideas, as all features found in these claims are directed towards mathematical calculations. The explanation for the rejection of Claims 2, 6, 10, 12, 14-17, 20-21, 28, 34, and 36-39 therefore are incorporated herein and applied to Claims 1, 8, and 23. These claims therefore stand rejected for similar reasons as explained in above Claims 1, 18, and 23.
Regarding claims 19 and 24,
The additional element of “wherein the operations GUI is further comprise generating a configured to display a generated diagnostic image of the target structure visually indicating any detected structural damage” is insignificant extra-solution (post-solution) activity that cannot reasonably integrate the judicial exception into a practical application (see MPEP 2106.05(g)).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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, 10, 12, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Ziehl (US 2020/0110041 A1, hereinafter referred to as “Ziehl”) in view of Le Duff (WO 2021/189130 A1, hereinafter referred to as “Le Duff”) further in view of Grodzki et al. (US 2015/0234024 A1, hereinafter referred to as “Grodzki”) and Menzel et al. (US 2014/0140374 A1, hereinafter referred to as “Menzel”).
Regarding claim 1, Ziehl teaches method for generalized fully sampled non-destructive evaluation/structural health monitoring (NDE/SHM) data, for use with different types of NDE/SHM systems (para. [0050]: FIG. 2 provides an example of acoustic emission (one type of SHM data); para. [0069]: a monitoring system can optionally include a non-destructive examination device or system for further examination of a structure), comprising
obtaining fully sampled data by conducting data acquisition relative to a target structure for non-destructive evaluation/structural health monitoring (para. [0052]: video data in correlation with SHM data over a predetermined period of time. For example, a preset number of hours, such as at least a 48-hour period, may be sampled).
Ziehl does not specifically teach obtaining undersampled data by applying a selected compression ratio to the fully sampled data, and conducting at least one of storing or transmitting the undersampled data using compressive sensing to reconstruct the fully sampled data from the undersampled data; analyzing the reconstructed fully sampled data; and using a graphical user interface (GUI) configured to: receive the fully sampled data as input; receive a selection of the compression ratio (CR); and display a visual comparison of the fully sampled data and the reconstructed fully sampled data to verify accuracy of the reconstruction.
fully sampled data to detect structural damage in the target structure (para. [0018]: video data may comprise a plurality of relatively high-definition images; and such processing system correlating may include correlating such sensor data and video data for a predetermined sample period of time; para. [0043]: the present disclosure is generally directed to a non-intrusive monitoring method and system for the detection of damage that may occur or may exist in an existing structure such as a bridge or other specialized structure; para. [0044]: More specifically, FIG. 1A represents a damaged (in this instance, broken) cable generally 10, notes that “video data” in para. [0018] and “a non-intrusive monitoring method and system for the detection of damage” in para. [0043] reads on “fully sampled data to detect structural damage in the target structure”.
However, Le Duff teaches obtaining undersampled data by applying a selected compression ratio to the fully sampled data (para. [0025]: the signals may be in response to acoustic signals transmitted by the probe into an object and may represent echo signals from the object. The received signals may provide N available samples. At 204, compressive sampling may be performed and a subset M of the N available samples may be taken, where N is greater than M (N>M)), and
conducting at least one of storing or transmitting the undersampled data (para. [0025]: the signals may be in response to acoustic signals transmitted by the probe into an object and may represent echo signals from the object. The received signals may provide N available samples. At 204, compressive sampling may be performed and a subset M of the N available samples may be taken, where N is greater than M (N>M); para. [0029]: the observation matrix b may be stored for later reconstruction, note that the above feature of N in para. [0025] reads on “undersampling”) usinq compressive sensing to reconstruct the fully sampled data (para. [0025]: N available samples) from the undersampled data (para. [0025]: subset M) (para. [0039]: FIG. 7 show the results of a TFM (total focusing method) reconstruction with compressive sampling);
analyzing the reconstructed fully sampled data (para. [0039]: FIG. 7 show the results of a TFM (total focusing method) reconstruction with compressive sampling; para. [0040]: FIG. 8 shows a comparison of TFM image generated using compressive sampling and one generated without use compressing sampling (e.g., full matrix sampling)); and
using a graphical user interface (GUI) configured to: receive the fully sampled data as input (para. [0039]: FIG. 7 show the results of a TFM (total focusing method) reconstruction with compressive sampling; para. [0040]: FIG. 8 shows a comparison of TFM image generated using compressive sampling and one generated without use compressing sampling (e.g., full matrix sampling)) ;
receive a selection of the compression ratio (CR) (para. [0040]: compressive sampling as described herein can reduce the data quantity to be transmitted (e.g., by a ratio = 22.5)); and
display a visual comparison of the fully sampled data and the reconstructed fully sampled data to verify accuracy of the reconstruction (para. [0039]: FIG. 7 show the results of a TFM (total focusing method) reconstruction with compressive sampling; para. [0040]: FIG. 8 shows a comparison of TFM image generated using compressive sampling and one generated without use compressing sampling (e.g., full matrix sampling), note that the above feature of “comparison of TFM image generated using compressive sampling and one generated without use compressing sampling (e.g., full matrix sampling)” reads on “display a visual comparison of the fully sampled data and the reconstructed fully sampled data to verify accuracy of the reconstruction”);
data undersampled in time only (para. [0025]: the signals may be in response to acoustic signals transmitted by the probe into an object and may represent echo signals from the object. The received signals may provide N available samples. At 204, compressive sampling may be performed and a subset M of the N available samples may be taken, where N is greater than M (N>M; para. [0029]: the acquired data may be provided as an A-scan, which refers to a time series of magnitude measurements versus time).
Ziehl and Le Duff are both considered to be analogous to the claimed invention because they are in the same filed of non-destructive sensing techniques. 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 the obtaining undersampled data and conducting at least one of storing or transmitting the undersampled data using compressive sensing such as are described in Le Duff into Ziehl, in order to allow non-destructive sensing techniques to be performed without significantly increasing hardware size or complexity (Le Duff, para. [0015]).
Ziehl and Le Duff do not specifically teach determining whether the undersampled data comprises data undersampled in both time and space, data undersampled in time only, or data undersampled in space only, and selectively routing the undersampled data through a multi-stage reconstruction dataflow based on said determining, wherein: data undersampled in both time and space flows through a first stage for temporal reconstruction and a second stage for spatial reconstruction, data undersampled in time only flows through only the first stage for temporal reconstruction, and data undersampled in space only bypasses the first stage and flows directly to the second stage for spatial reconstruction.
However, Grodzki teaches determining whether the undersampled data comprises data undersampled in both time and space or data undersampled in space only (para. [0021]: the same k-space point can be determined multiple times as a k-space point to be sampled and therefore sampled. According to a second variant, the same k-space point cannot be determined multiple times; para. [0029]: This step of moving the k-space points to be sampled onto the FFT grid is carried out before any optimization of the sequence in which the k-space points to be sampled are ultimately sampled …the courses of the gradients (in the acquisition of the MR data) increase dramatically from time-step to time-step; para. [0034]: The computational computer is designed to determine randomized k-space points to be sampled so that k-space is undersampled, note that the above feature of “K-space” in paras. [0021] and “K-point with time step” in para. [0029] reads on “determining whether the undersampled data comprises data undersampled in both time and space or data undersampled in space only”),
wherein: data undersampled in both time and space flows through a first stage for temporal reconstruction (para. [0008]: an MR image is reconstructed as a function of the MR data acquired, note that the above feature of “reconstructing as a function of the MR data acquired” reads on “temporal reconstruction”) and a second stage for spatial reconstruction, data undersampled in time only flows through only the first stage for temporal reconstruction, (para. [0008]: an MR image is reconstructed as a function of the MR data acquired, note that the above feature of “reconstructing as a function of the MR data acquired” reads on “temporal reconstruction”) and data undersampled in space only bypasses the first stage and flows directly to the second stage for spatial reconstruction (para. [0021]: the same k-space point can be determined multiple times as a k-space point to be sampled and therefore sampled. According to a second variant, the same k-space point cannot be determined multiple times; para. [0034]: The computational computer is designed to determine randomized k-space points to be sampled so that k-space is undersampled, note that the above feature of “fist stage and second stage correspond to the same k-space point that can be determined multiple times” and “the same k-space point that cannot be determined multiple times” in para. [0021] and “k-space is undersampled” in para. [0034] reads on “data undersampled in both time and space” and “data undersampled in space or data undersampled in space only,” respectively).
Ziehl and Grodzki are both considered to be analogous to the claimed invention because they are in the same filed of determining points in the raw data space. 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 the determining whether the undersampled data comprises data undersampled in both time and space, data undersampled in time only or data undersampled in space only such as is described in Grodzki into Ziehl, in order to provide a method for the acquisition of data from a pre-determined two-dimensional volume segment or from a pre-determined slice within an examination object by operation (Grodzki, para. [0007]).
Ziehl, Le Duff, and Grodzki do not specifically teach selectively routing the data through a multi-stage reconstruction dataflow based on said determining.
However, Menzel teaches selectively routing the data through a multi-stage reconstruction dataflow based on said determining (para. [0032]: The system has a first transmitting data node and a second receiving data node and at least two different communication routes for transmitting data between the first and the second data node; para. [0050]: a redundancy of the data is generated since a complete reconstruction of the data is possible even if only one of the three communication routes a, b, c is working).
Ziehl and Menzel are both considered to be analogous to the claimed invention because they are in the same filed of transmitting and receiving data node. 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 the selectively routing the data through a multi-stage reconstruction dataflow such as is described in Menzel into Ziehl, in order to improve the data security in a data transmission (Menzel, para. [0010]).
Regarding claim 6, Ziehl in view of Le Duff, Grodzki, and Menzel teaches all the limitation of claim 1, in addition, Le Duff teaches further comprising using a compression ratio (CR) value of at least 20% (para. [0040]: compressive sampling as described herein can reduce the data quantity to be transmitted (e.g., by a ratio = 22.5)).
Ziehl and Le Duff are both considered to be analogous to the claimed invention because they are in the same filed of non-destructive sensing techniques. 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 the compression ratio (CR) value such as is described in Le Duff into Ziehl, in order to allow non-destructive sensing techniques to be performed without significantly increasing hardware size or complexity (Le Duff, para. [0015]).
Regarding claim 10, Ziehl in view of Le Duff, Grodzki, and Menzel teaches all the limitation of claim 1, in addition, Ziehl teaches that conducting data acquisition comprises conducting pitch-catch (para. [0050]: Structural Health Monitoring (SHM) data showing amplitude of waveforms versus time), ultrasonic guided-wave structural health monitoring (SHM) of the target structure (para. [0069]: a structure that may have been affected by the potentially damaging event can be further analyzed by use of, and without limitation to, ultrasonic techniques; para. [0072]: a monitoring system can include components of a passive SHM system and/or an active SHM system on or in or affixed to a structure that is in contact with a sensor).
Regarding claim 12, Ziehl in view of Le Duff, Grodzki, and Menzel teaches all the limitation of claim 1, in addition, Ziehl teaches that conducting data acquisition comprises obtaining pulse-echo/A-scan data or acoustic emission data of the target structure (para. [0018]: sensor may be an acoustic emission sensor which may be one of attached to, embedded in, or associated with the structure to be monitored; para. [0032]: at least one of acoustic wave sensor).
Regarding claim 15, Ziehl in view of Le Duff, Grodzki, and Menzel teaches all the limitation of claim 1, in addition, Ziehl teaches that conducting data acquisition comprises at least one of conducting pitch-catch (para. [0050]: Structural Health Monitoring (SHM) data showing amplitude of waveforms versus time; para. [0069]: a structure that may have been affected by the potentially damaging event can be further analyzed by use of, and without limitation to, ultrasonic techniques), ultrasonic guided-wave structural health monitoring (SHM) of the target structure (para. [0072]: a monitoring system can include components of a passive SHM system and/or an active SHM system on or in or affixed to a structure that is in contact with a sensor), or conducting pulse-echo (A-scan), B- scan, C-scan, Z-scan, acoustic emission, impact data, thermography, or other non-destructive evaluation/structural health monitoring techniques.
Claims 2 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Ziehl (US 2020/0110041 A1, hereinafter referred to as “Ziehl”) in view of Le Duff, Grodzki, Menzel, and Cella et al. (US 2020/0201292 A1, hereinafter referred to “Cella”).
Regarding claim 2, Ziehl in view of Le Duff, Grodzki, and Menzel teaches all the limitation of claim 1,
Ziehl, Le Duff, Grodzki, and Menzel do not specifically teach generating a diagnostic image of the target structure visually indicating any detected structural damage.
However, Cellar teaches further comprising generating a diagnostic image of the target structure visually indicating any detected structural damage (para. [0200]: an operating deflection shape can be created that can show dynamic movements of the machine in 3 D, which can provide an invaluable diagnostic tool).
Ziehl and Cella are both considered to be analogous to the claimed invention because they are in the same filed of data collection in industrial environments. 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 the generating the diagnostic image of the target structure such as is described in Cella into Ziehl, in order to use collected data to provide improved monitoring, control, and intelligent diagnosis of problems and intelligent optimization of operations in various heavy industrial environments (Cella, para. [0010]).
Regarding claim 16, Ziehl in view of Le Duff, Grodzki, and Menzel teaches all the limitation of claim 1.
Ziehl, Le Duff, Grodzki, and Menzel not specifically teach that the fully sampled data and the undersampled data is are both respectively in a data format including at least one of ASCII, binary, and comma-separated values (CSV) data files.
However, Cellar teaches the fully sampled data and the undersampled data (para. [0192]: para. [0192]: sample waveform at 100 Hz can be undersampled at every tenth point of the digital waveform to produce an effective sampling rate of 10 Hz, note that above feature of “sampled data by 100 Hz” and “sampled data by 10 Hz” in para. [0192] reads on “fully sampled data” and “undersampled data,” respectively) is are both respectively in a data format including at least one of ASCII, binary, and comma-separated values (CSV) data files (para. [0221]: the TDMS file format can be optimized for streaming various types of measurement data (i.e., binary digital samples of waveforms); para. [0222[: the many embodiments include a hybrid relational metadata-binary storage approach (HRM-BSA).
Ziehl and Cella are both considered to be analogous to the claimed invention because they are in the same filed of data collection in industrial environments. 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 the fully sampled data and the undersampled data such as is described in Cella into Ziehl, in order to use collected data to provide improved monitoring, control, and intelligent diagnosis of problems and intelligent optimization of operations in various heavy industrial environments (Cella, para. [0010]).
Claims 18-21, 23-24, 28, 32, 34, 36-39 are rejected under 35 U.S.C. 103 as being unpatentable over Ziehl in view of, Cella, Reed (US 2019/0204579 A1, hereinafter referred to as “Reed”), Le Duff, Grodzki, and Menzel.
Regarding claim 18, Ziehl in view of Grodzki, Menzel, Cella and Reed teaches methodology for non-destructive evaluation/structural health monitoring (NDE/SHM) of structures based on pitch-catch ultrasonic guided waves, using a degree of data acquisition from a target structure that is relatively reduced from what is required for a full sampling of the target structure, while maintaining the ability to accurately detect, locate, and characterize damage to the target structure (paras. [0050] and [0069] : ultrasonic techniques of Ziehl; para. [0192] of Cella; para. [0022] of Reed), comprising:
Zeihl teaches obtaining a set of fully sampled sensor signals from actuator-sensor paths of the target structure (para. [0060]: a plurality of sensors 24 can be arranged on a surface 26, for instance in an array or grid. Depending upon the nature of the sensors 24 and the surface 26, a system can include current paths or signal lines to and from each sensor 24, for example via wires that physically attach the sensors 24 to one another. The thus-formed network can serve to aggregate signals from sensors 24, note the above feature of “sessors 24” and “ surface 26” reads on “ sensor signals” and “target structure,” respectively) and pitch-catch ultrasonic guided wave data (para. [0069]: ultrasonic techniques; para, [0070]: ultrasonic transducers).
Zeihl does not specifically teaches subjecting the set of fully sampled sensor signals to a selected compression ratio (CR) to obtain undersampled sensor signals; analyzing the fully sampled sensor signals to detect any structural damage to the target structure; and using a graphical user interface (GUI) for inputting the set of fully sampled sensor signals).
However, Cellar teaches subjecting the set of undersampled sensor signals to compressive sensing to reconstruct fully sampled sensor signals (para. [0192]: sample waveform at 100 Hz can be undersampled at every tenth point of the digital waveform to produce an effective sampling rate of 10 Hz);
analyzing the fully sampled sensor signals to detect any structural damage to the target structure (para. [0025]: mathematical reconstruction is used to calculate the video image that would have been observed at each of the referenced points, note that the above feature of “observed at each of the referenced points” reads on “analyzing the reconstructed fully sampled data to detect structural damage in the target structure”).
Ziehl and Cella are both considered to be analogous to the claimed invention because they are in the same filed of data collection in industrial environments. 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 the subjecting the set of undersampled sensor signals, analyzing the fully sampled sensor signals, using a graphical user interface (GUI) such as are described in Cella into Ziehl, in order to use collected data to provide improved monitoring, control, and intelligent diagnosis of problems and intelligent optimization of operations in various heavy industrial environments (Cella, para. [0010]).
Ziehl and Cella do not specifically teach subjecting the set of fully sampled sensor signals to a selected compression ratio (CR) to obtain undersampled sensor signals, reconstructed fully sampled sensor signal, and analyzing the reconstructed fully sampled sensor signals to detect any structural damage to the target structure.
However, Reed teaches subjecting the set of fully sampled sensor signals to a selected compression ratio (CR) (para. [0030]: compression ratio) to obtain undersampled sensor signals (para. [0025]: temporal compressive sensing methods, i.e., in which signals are reconstructed using data sets that under-sample the signal in the time domain);
reconstructed fully sampled sensor signal (para. [0025]: temporal compressive sensing methods, i.e., in which signals are reconstructed using data sets that under-sample the signal in the time domain) and
analyzing the reconstructed fully sampled sensor signals to detect any structural damage to the target structure (para. [0025]: temporal compressive sensing methods, i.e., in which signals are reconstructed using data sets that under-sample the signal in the time domain …mathematical reconstruction is used to calculate the video image that would have been observed at each of the referenced points, note that the above feature of “mathematical reconstruction” and “observed at each of the referenced points” reads on “analyzing the reconstructed fully sampled data to detect structural damage in the target structure”).
Ziehl and Reed are both considered to be analogous to the claimed invention because they are in the same filed of data acquisition. 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 the subjecting the set of fully sampled sensor signals to a selected compression ratio (CR), analyzing the reconstructed fully sampled sensor signals such as are described in Reed into Ziehl, in order to reconstruct the signal based on a limited series of test measurements (Reed, para. [0003]).
Ziehl, Cellar, Reed do not specifically teach using a graphical user interface (GUI) configured to: receive the fully sampled data as input; receive a selection of the compression ratio (CR) and display a visual comparison of the fully sampled data and the reconstructed fully sampled data to verify accuracy of the reconstruction.
However, Le Duff. Teaches using a graphical user interface (GUI) configured to: receive the fully sampled data as input (para. [0039]: FIG. 7 show the results of a TFM (total focusing method) reconstruction with compressive sampling; para. [0040]: FIG. 8 shows a comparison of TFM image generated using compressive sampling and one generated without use compressing sampling (e.g., full matrix sampling));
receive a selection of the compression ratio (CR) (para. [0040]: compressive sampling as described herein can reduce the data quantity to be transmitted (e.g., by a ratio = 22.5)); and
display a visual comparison of the fully sampled data and the reconstructed fully sampled data to verify accuracy of the reconstruction (para. [0039]: FIG. 7 show the results of a TFM (total focusing method) reconstruction with compressive sampling; para. [0040]: FIG. 8 shows a comparison of TFM image generated using compressive sampling and one generated without use compressing sampling (e.g., full matrix sampling), note that the above feature of “comparison of TFM image generated using compressive sampling and one generated without use compressing sampling (e.g., full matrix sampling)” reads on “display a visual comparison of the fully sampled data and the reconstructed fully sampled data to verify accuracy of the reconstruction”).
Ziehl and Le Duff are both considered to be analogous to the claimed invention because they are in the same filed of non-destructive sensing techniques. 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 the graphical user interface such as is described in Le Duff into Ziehl, in order to allow non-destructive sensing techniques to be performed without significantly increasing hardware size or complexity (Le Duff, para. [0015]).
Ziehl, Cella, Reed, and Le Duff do not specifically teach undersampled in both time and space.
However, Grodzki teaches undersampled in both time and space
(para. [0021]: the same k-space point can be determined multiple times as a k-space point to be sampled and therefore sampled; para. [0029]: This step of moving the k-space points to be sampled onto the FFT grid is carried out before any optimization of the sequence in which the k-space points to be sampled are ultimately sampled …the courses of the gradients (in the acquisition of the MR data) increase dramatically from time-step to time-step para. [0034]: The computational computer is designed to determine randomized k-space points to be sampled so that k-space is undersampled, note that the above feature of “K-space” in paras. [0021] and [0034] reads on “undersampled in both time and space”).
Ziehl and Grodzki are both considered to be analogous to the claimed invention because they are in the same filed of determining points in the raw data space. 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 the undersampled in both time and space such as is described in Grodzki into Ziehl, in order to provide a method for the acquisition of data from a pre-determined two-dimensional volume segment or from a pre-determined slice within an examination object by operation (Grodzki, para. [0007]).
Ziehl, Le Duff, and Grodzki do not specifically teach routing the sensor signals through a multi-stage reconstruction dataflow, wherein the sensor signals are routed through both a first stage for temporal reconstruction and a second stage for spatial reconstruction based on the sensor signals.
However, Menzel teaches routing the sensor signals through a multi-stage reconstruction dataflow, wherein the sensor signals are routed through both a first stage for temporal reconstruction and a second stage for spatial reconstruction based on the sensor signals (para. [0032]: The system has a first transmitting data node and a second receiving data node and at least two different communication routes for transmitting data between the first and the second data node; para. [0050]: a redundancy of the data is generated since a complete reconstruction of the data is possible even if only one of the three communication routes a, b, c is working).
Ziehl and Menzel are both considered to be analogous to the claimed invention because they are in the same filed of transmitting and receiving data node. 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 the routing the sensor signals such as is described in Menzel into Ziehl, in order to improve the data security in a data transmission (Menzel, para. [0010]).
Regarding claim 19, Ziehl in view of Grodzki, Menzel, Cella, Reed, and Le Duff teaches all the limitation of claim 18, in addition, Cella teaches generating a diagnostic image of the target structure visually indicating any detected structural damage (Para. [0034]: the local data collection system includes a graphical user interface (“GUI”) system configured to manage the data collection bands. In embodiments, the GUI system includes an expert system diagnostic tooI).
Ziehl and Cella are both considered to be analogous to the claimed invention because they are in the same filed of data collection in industrial environments. 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 the generating the diagnostic image of the target structure such as is described in Cella into Ziehl, in order to use collected data to provide improved monitoring, control, and intelligent diagnosis of problems and intelligent optimization of operations in various heavy industrial environments (Cella, para. [0010]).
Regarding claim 20, Ziehl in view of Grodzki, Menzel, Cella, Reed, and Le Duff teaches all the limitation of claim 18, in addition, Reed teaches generating basis functions for conducting the compressive sensing (paras. [0022]-[0023]: compressive sensing to reduce the amount of data storage required).
Ziehl and Reed are both considered to be analogous to the claimed invention because they are in the same filed of data acquisition. 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 the compressive sensing such as is described in Reed into Ziehl, in order to reconstruct the signal based on a limited series of test measurements (Reed, para. [0003]).
Regarding claim 21, Ziehl in view of Grodzki, Menzel, Cella, Reed, and Le Duff teaches all the limitation of claim 18, in addition, Reed teaches that a compression ratio (CR) has a value of at least 20% (para. [0030]: data compression ratio) .
Ziehl and Reed are both considered to be analogous to the claimed invention because they are in the same filed of data acquisition. 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 the compressive ratio such as is described in Reed into Ziehl, in order to reconstruct the signal based on a limited series of test measurements (Reed, para. [0003]).
Regarding claim 23, it is a non-destructive evaluation/structural health monitoring (NDE/SHM) system type claim having similar limitations as of claim 1 above. Therefore, it is rejected under the same rationale as of claim 1 above.
Regarding claim 24, it is dependent on claim 23 and has similar limitations as of claim 2 above. Therefore, it is rejected under the same rational as of claim 2 above.
Regarding claim 28, it is dependent on claim 23 and has similar limitations as of claim 6 above. Therefore, it is rejected under the same rational as of claim 6 above.
Regarding claim 32, it is dependent on claim 23 and has similar limitations as of claim 10 above. Therefore, it is rejected under the same rational as of claim 10 above.
Regarding claim 34, it is dependent on claim 23 and has similar limitations as of claim 12 above. Therefore, it is rejected under the same rational as of claim 12 above.
Regarding claim 36, it is dependent on claim 23 and has similar limitations as of claim 14 above. Therefore, it is rejected under the same rational as of claim 14 above.
Regarding claim 37, it is dependent on claim 23 and has similar limitations as of claim 15 above. Therefore, it is rejected under the same rational as of claim 15 above.
Regarding claim 38, it is dependent on claim 23 and has similar limitations as of claim 16 above. Therefore, it is rejected under the same rational as of claim 16 above.
Regarding claim 39, it is dependent on claim 24 and has similar limitations as of claim 17 above. Therefore, it is rejected under the same rational as of claim 17 above.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Ziehl in view of Le Duff, Grodzki, Menzel, and Bing et al. (CN 101672634 A, hereinafter referred to as “Bing”).
Regarding claim 14, Ziehl in view of Le Duff, Grodzki, and Menzel teaches all the limitation of claim 1,
Ziehl, Le Duff, Grodzki, and Menzel do not specifically teach conducting data acquisition comprises obtaining C-scan data of the target structure.
However, Bing teaches conducting data acquisition comprises obtaining C-scan data of the target structure (page 6, lines 4-5: each point of measured target detection position outputs on the screen, arranges in order, realizes the structure of C scan image; page 7, lines 24-25 :obtain the gradation of image value of each point of measured target detection position according to the step of 1-6, these pixels are arranged in order, realize the structure of C scan image).
Ziehl and Bing are both considered to be analogous to the claimed invention because they are in the same filed of ultrasonic scanning comprising image. 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 the obtaining C-scan data of the target structure such as is described in Bing into Ziehl, in order to provide a kind of C-scan peak image of ultrasonic scanning microscope and the C scan image that makes up is clear, the feature that can reflect device inside in detail has advantages such as the design of graphics image position is accurate, precision is high, clear picture (Bing, page 2, lines 32-34).
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Ziehl in view of Le Duff, Grodzki, Menzel, Cella, and McKenna et al. (US 9,558,554 B1, hereinafter referred to as “McKenna”).
Regarding claim 17, Ziehl in view of Grodzki, Menzel, Le Duff, and Cella teaches all the limitation of claim 2. Ziehl, Le Duff, and Cella do not specifically teaches generating a probability-of-detection (POD) curve as a function of compression ratio (CR).
However, McKenna teaches generating a probability-of-detection (POD) curve as a function of compression ratio (CR) (col. 6, lines 34-30: if a probability of this occurring by chance under the Gaussian random field model (i.e., the p-value) is sufficiently low, the hypothesis that the original and mean reconstructed images are the same is rejected, and the number of basis functions in the solution space needs to be increased, note that the above feature of “p-value” and “the number of basis functions in the solution space needs to be increased” reads on “a probability-of-detection (POD) curve as a function of compression ratio (CR)”), and
a determined correlation coefficient (r) after reconstruction to quantify accuracy of damage detection from the reconstructed fully sampled data (col. 10, lines 38-40: the present disclosure enables a quantifiable means of determining the accuracy of a reconstructed image relative to a reference image; col. 5, lines 26-31: the application of a local univariate statistical test (e.g., a t-test) 160 to the difference image 150 results in generating a Test Statistic Image 170. It is understood that statistical test 165 may be any univariate statistical test including t-test, F-test, correlation coefficient, etc, note that the above feature of “determining the accuracy of a reconstructed image relative to a reference image” in col. 10, lines 38-40 and “any univariate statistical test including t-test, F-test, correlation coefficient” in col. 5. Lines 26-31” reads on “ correlation coefficient (r) after reconstruction to quantify accuracy of damage detection from the reconstructed fully sampled data”).
Ziehl and McKenna are both considered to be analogous to the claimed invention because they are in the same filed of measuring image data. 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 the generating the probability-of-detection (POD) curve and the determined correlation coefficient such as are described in McKenna into Ziehl, in order to develop quantitative measures of image accuracy that allow for determining significant deviations between a reference image and a reconstructed image (col. 1, lines 63-66).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Samsonov et al. (US 2016/0071291 A1) teaches a system and method for reconstructing a series of images of a subject includes acquiring medical image data from the subject with a medical imaging system and reconstructing a series of images of the subject from the acquired medical image data set. The reconstructing includes enforcing general adherence to a non-patient-specific signal model that describes a dependency of image intensity values on at least one variable that is associated with a physical or physiological property by constraining reconstruction of individual images in the series of images using the non-patient-specific model.
Otazo et al. (US 10,605,878 B2) teaches that exemplary method, system and computer-accessible medium can be provided which facilitates an acquisition of radial data, which can be continuous, with an exemplary golden-angle procedure and reconstruction with arbitrary temporal resolution at arbitrary time points.
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.
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/SANGKYUNG LEE/Examiner, Art Unit 2858
/LEE E RODAK/Supervisory Patent Examiner, Art Unit 2858
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