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 .
Response to Amendment
The amendment filed 03/26/2026 has been entered.
Claim 24 is new.
Claims 5, 17, 19 and 22 are cancelled.
Claims 1, 4, 8, 15, 18 and 21 are amended.
Claims 1-4, 6-16, 18, 20-21 and 23-24 are pending.
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 (i.e., changing from AIA to pre-AIA ) 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 1-4, 6-16, 18, 20 and 23-24 are rejected under 35 U.S.C. 103 as being unpatentable over Zhu (US 2021/0270127 A1) in view of Kalyanraman (US 10,539,6991 and Birchak (US 5,924,499 A) and further evidenced by Ranganathan (US 2023/0204424 A1).
Regarding claim 1, Zhu teaches a processor[0105 has a processor];
and a non-transitory computer-readable medium comprising instructions that are executable by the processor for causing the processor to perform operations comprising[0105 has computer]:
receiving ultrasonic waveform data about a wellbore at a plurality of time intervals during a wellbore operation using an arrangement of transducers positionable in the wellbore[Abstract; Figs 1-3, 0037, 0050, Claim 1 has sonic signals being received, 0047, 0051 and Claim 9 have data over time meaning various time intervals], the ultrasonic waveform data including a plurality of ultrasonic waveforms[0034, 0060, 0070, has ultrasonic being part of sonic]; …..
determining a time window for generating a set of attributes of each ultrasonic waveform of the plurality of ultrasonic waveforms, wherein the operation of determining the time window comprises: setting an initial time of the time window at a time of receiving a primary flexural wave of the flexural waves[0051 has processing of data to generate attributes such as slowness over time/frequency/space; 0047 has time windows of returns being processed meaning the start of returns are being processed] ;
setting a final time of the time window at a predetermined subsequent time that is chronologically before an end of the ultrasonic waveform data; [0051 has processing of data to generate attributes such as slowness over time/frequency/space; 0047 has time windows of returns being processed meaning there will be an end of returns are being processed; Moreover processing of returns and selection of any arbitrarily chosen time smaller than the total returns means that the time window naturally less than the end of the data and any processing ultrasonic waveform data that needs as there no point in processing data that does not contain returns]
generating the set of attributes using the time window[0051 has processing of data to generate attributes such as slowness over time/frequency/space; 0047 has time windows to of returns being processed];…..
and extracting the set of attributes from the amplitudes and phases of each segment of the one or more segments, wherein each ultrasonic waveform of the plurality of ultrasonic waveforms indicates a plurality of attributes;[0051 has processing of data to generate attributes such as slowness over time/frequency/space;]
determining using the set of attributes as input to a predetermined optimization operation, a number of annular conditions behind a casing of the wellbore that are able to be clustered [0056-0058 has selection of cluster identifiers and condition of cement is in 0058, 0057 also has selection of number of clusters as well as objective function being defined and used to determine number of clusters]
determining, a number of clusters to use in a clustering operation involving the set of attributes, [0084 has optimization method; 0049, 0053-0057 has choosing number of clusters which would read on determining number of clusters] wherein the number of clusters is determinable based on the number of annular conditions behind the casing of the wellbore that are able to be clustered[0084 has optimization method; 0049, 0053-0057 has choosing number of clusters which would read on determining number of clusters; See also Fig 4 which has clusters and optimization to identify annular conditions];
applying, for each time interval included in the plurality of different time intervals and using the number of clusters, an unsupervised machine-learning model to the plurality of ultrasonic waveforms for clustering, using the clustering operation the set of attributes of each ultrasonic waveform in the time interval into a set of clusters[0049, 0052 0054, Claim 1 has grouping/clustering using an unsupervised machine learning system for clustering], wherein each cluster of the set of clusters represents a different annular condition of a set of annular conditions possible in the wellbore[0049, 0052-0054, Claim 1 has grouping/clustering using an unsupervised machine learning system for clustering; Abstract, 0033 also has cement condition in the annulus being characterized meaning its an annular condition; 0058 and fig 4 also has clustering and cement condition in annulus]
outputting the set of clusters for categorizing the ultrasonic waveform data for distinguishing between channels and microannuli in the wellbore based on the set of annular conditions. [0033, 0037, Claim 1 has outputting the clusters which show wellbore properties such as cement condition and well integrity which relates to channels in the wellbore; Abstract, 0033 also has cement condition in the annulus being characterized meaning it's an annular condition; 0058 and fig 4 also has clustering and cement condition in annulus]
and based on updated cluster assignments in the set of clusters for identifying changes in the set of annular conditions over the plurality of different time intervals, determining a first annular condition of the set of annular conditions that is a persistent channel and a second annular condition of the set of annular conditions that is a transient microannuli in the wellbore[0033, 0037, Claim 1 has outputting the clusters which show wellbore properties such as cement condition and well integrity which relates to channels in the wellbore; Abstract, 0033 also has cement condition in the annulus being characterized meaning it's an annular condition; 0058 and fig 4 also has clustering and cement condition in annulus, 0047, 0051 and Claim 9 have data over time meaning monitoring changes over time ]
While Zhu implies, the data is ultrasonic, Kalyanraman teaches receiving ultrasonic waveform data about a wellbore using an arrangement of transducers positionable in the wellbore[Abstract, Fig 1; Claim 1], the ultrasonic waveform data including a plurality of ultrasonic waveforms [ Abstract, Fig 1; Claim 1]; the transducers tilted in a pitch-catch arrangement to cause flexural waves to be generated in the wellbore[Col 5; Lines 30-40 has pitch catch arrangement and flexural waves];.....and outputting the set of clusters for categorizing the ultrasonic waveform data for distinguishing between channels and microannuli in the wellbore based on the set of annular conditions. [Col 7, Lines 35-40; Col 8, Line 55- Col 9, Line 5; Fig 5, 6, Col 10 Lines 30-35 and 50-55 all have distinguishing channels and microannuli based on the acoustics].
Birchak teaches dividing the time window into one or more segments 19; Lines 35-40 and Claims 26, 28, 33 and 34 have signals in various time windows for processing received data is divided into parts for processing];
combining amplitudes and phases of the primary flexural wave of each segment of the one or more segments[ 14; Lines 45-60 have sum of signals and amplitudes and phases];
It would have been obvious to one of ordinary skill in the art before the filing date to have modified the sonic waves in Zhu to use ultrasonic waves as ultrasonic tools are well known in the art and to use the pitch catch arrangement and flexural waves of Kalyanraman with the various segments and time windows and combined amplitude and phases of Birchak to distinguish between channels and microannuli to get better wellbore data by processing the ultrasonic waveform data.
Moreover, it would have been obvious to one having ordinary skill in the art before the filing date to have used various known methods to determine an optimum number of clusters, attributes and segments in machine learning, since it has been held that where routine testing and general experimental conditions are present, discovering the optimum values or workable ranges until the desired effect is achieved involves only routine skill in the art. See, Inre Aller, 105 USPQ 233.
Regarding claim 8, Zhu teaches receiving ultrasonic waveform data about a wellbore at a plurality of time intervals during a wellbore operation using an arrangement of transducers positionable in the wellbore[Abstract; Figs 1-3, 0037, 0050, Claim 1 has sonic signals being received, 0047, 0051 and Claim 9 have data over time meaning various time intervals], the ultrasonic waveform data including a plurality of ultrasonic waveforms[0034, 0060, 0070, has ultrasonic being part of sonic]; …..
determining a time window for generating a set of attributes of each ultrasonic waveform of the plurality of ultrasonic waveforms, wherein the operation of determining the time window comprises: setting an initial time of the time window at a time of receiving a primary flexural wave of the flexural waves[0051 has processing of data to generate attributes such as slowness over time/frequency/space; 0047 has time windows of returns being processed meaning the start of returns are being processed] ;
setting a final time of the time window at a predetermined subsequent time that is chronologically before an end of the ultrasonic waveform data; [0051 has processing of data to generate attributes such as slowness over time/frequency/space; 0047 has time windows of returns being processed meaning there will be an end of returns are being processed; Moreover processing of returns and selection of any arbitrarily chosen time smaller than the total returns means that the time window naturally less than the end of the data and any processing ultrasonic waveform data that needs as there no point in processing data that does not contain returns]
generating the set of attributes using the time window[0051 has processing of data to generate attributes such as slowness over time/frequency/space; 0047 has time windows to of returns being processed];…..
and extracting the set of attributes from the amplitudes and phases of each segment of the one or more segments, wherein each ultrasonic waveform of the plurality of ultrasonic waveforms indicates a plurality of attributes;[0051 has processing of data to generate attributes such as slowness over time/frequency/space;]
determining using the set of attributes as input to a predetermined optimization operation, a number of annular conditions behind a casing of the wellbore that are able to be clustered [0056-0058 has selection of cluster identifiers and condition of cement is in 0058, 0057 also has selection of number of clusters as well as objective function being defined and used to determine number of clusters]
determining, a number of clusters to use in a clustering operation involving the set of attributes, [0084 has optimization method; 0049, 0053-0057 has choosing number of clusters which would read on determining number of clusters] wherein the number of clusters is determinable based on the number of annular conditions behind the casing of the wellbore that are able to be clustered[0084 has optimization method; 0049, 0053-0057 has choosing number of clusters which would read on determining number of clusters; See also Fig 4 which has clusters and optimization to identify annular conditions];
applying, for each time interval included in the plurality of different time intervals and using the number of clusters, an unsupervised machine-learning model to the plurality of ultrasonic waveforms for clustering, using the clustering operation the set of attributes of each ultrasonic waveform in the time interval into a set of clusters[0049, 0052 0054, Claim 1 has grouping/clustering using an unsupervised machine learning system for clustering], wherein each cluster of the set of clusters represents a different annular condition of a set of annular conditions possible in the wellbore[0049, 0052-0054, Claim 1 has grouping/clustering using an unsupervised machine learning system for clustering; Abstract, 0033 also has cement condition in the annulus being characterized meaning its an annular condition; 0058 and fig 4 also has clustering and cement condition in annulus]
outputting the set of clusters for categorizing the ultrasonic waveform data for distinguishing between channels and microannuli in the wellbore based on the set of annular conditions. [0033, 0037, Claim 1 has outputting the clusters which show wellbore properties such as cement condition and well integrity which relates to channels in the wellbore; Abstract, 0033 also has cement condition in the annulus being characterized meaning it's an annular condition; 0058 and fig 4 also has clustering and cement condition in annulus]
and based on updated cluster assignments in the set of clusters for identifying changes in the set of annular conditions over the plurality of different time intervals, determining a first annular condition of the set of annular conditions that is a persistent channel and a second annular condition of the set of annular conditions that is a transient microannuli in the wellbore[0033, 0037, Claim 1 has outputting the clusters which show wellbore properties such as cement condition and well integrity which relates to channels in the wellbore; Abstract, 0033 also has cement condition in the annulus being characterized meaning it's an annular condition; 0058 and fig 4 also has clustering and cement condition in annulus, 0047, 0051 and Claim 9 have data over time meaning monitoring changes over time ]
While Zhu implies, the data is ultrasonic, Kalyanraman teaches receiving ultrasonic waveform data about a wellbore using an arrangement of transducers positionable in the wellbore[Abstract, Fig 1; Claim 1], the ultrasonic waveform data including a plurality of ultrasonic waveforms [ Abstract, Fig 1; Claim 1]; the transducers tilted in a pitch-catch arrangement to cause flexural waves to be generated in the wellbore[Col 5; Lines 30-40 has pitch catch arrangement and flexural waves];.....and outputting the set of clusters for categorizing the ultrasonic waveform data for distinguishing between channels and microannuli in the wellbore based on the set of annular conditions. [Col 7, Lines 35-40; Col 8, Line 55- Col 9, Line 5; Fig 5, 6, Col 10 Lines 30-35 and 50-55 all have distinguishing channels and microannuli based on the acoustics].
Birchak teaches dividing the time window into one or more segments 19; Lines 35-40 and Claims 26, 28, 33 and 34 have signals in various time windows for processing received data is divided into parts for processing];
combining amplitudes and phases of the primary flexural wave of each segment of the one or more segments[ 14; Lines 45-60 have sum of signals and amplitudes and phases];
It would have been obvious to one of ordinary skill in the art before the filing date to have modified the sonic waves in Zhu to use ultrasonic waves as ultrasonic tools are well known in the art and to use the pitch catch arrangement and flexural waves of Kalyanraman with the various segments and time windows and combined amplitude and phases of Birchak to distinguish between channels and microannuli to get better wellbore data by processing the ultrasonic waveform data.
Moreover, it would have been obvious to one having ordinary skill in the art before the filing date to have used various known methods to determine an optimum number of clusters, attributes and segments in machine learning, since it has been held that where routine testing and general experimental conditions are present, discovering the optimum values or workable ranges until the desired effect is achieved involves only routine skill in the art. See, Inre Aller, 105 USPQ 233.
Regarding claim 15, Zhu teaches receiving ultrasonic waveform data about a wellbore at a plurality of time intervals during a wellbore operation using an arrangement of transducers positionable in the wellbore[Abstract; Figs 1-3, 0037, 0050, Claim 1 has sonic signals being received, 0047, 0051 and Claim 9 have data over time meaning various time intervals], the ultrasonic waveform data including a plurality of ultrasonic waveforms[0034, 0060, 0070, has ultrasonic being part of sonic]; …..
determining a time window for generating a set of attributes of each ultrasonic waveform of the plurality of ultrasonic waveforms, wherein the operation of determining the time window comprises: setting an initial time of the time window at a time of receiving a primary flexural wave of the flexural waves[0051 has processing of data to generate attributes such as slowness over time/frequency/space; 0047 has time windows of returns being processed meaning the start of returns are being processed] ;
setting a final time of the time window at a predetermined subsequent time that is chronologically before an end of the ultrasonic waveform data; [0051 has processing of data to generate attributes such as slowness over time/frequency/space; 0047 has time windows of returns being processed meaning there will be an end of returns are being processed; Moreover processing of returns and selection of any arbitrarily chosen time smaller than the total returns means that the time window naturally less than the end of the data and any processing ultrasonic waveform data that needs as there no point in processing data that does not contain returns]
generating the set of attributes using the time window[0051 has processing of data to generate attributes such as slowness over time/frequency/space; 0047 has time windows to of returns being processed];…..
and extracting the set of attributes from the amplitudes and phases of each segment of the one or more segments, wherein each ultrasonic waveform of the plurality of ultrasonic waveforms indicates a plurality of attributes;[0051 has processing of data to generate attributes such as slowness over time/frequency/space;]
determining using the set of attributes as input to a predetermined optimization operation, a number of annular conditions behind a casing of the wellbore that are able to be clustered [0056-0058 has selection of cluster identifiers and condition of cement is in 0058, 0057 also has selection of number of clusters as well as objective function being defined and used to determine number of clusters]
determining, a number of clusters to use in a clustering operation involving the set of attributes, [0084 has optimization method; 0049, 0053-0057 has choosing number of clusters which would read on determining number of clusters] wherein the number of clusters is determinable based on the number of annular conditions behind the casing of the wellbore that are able to be clustered[0084 has optimization method; 0049, 0053-0057 has choosing number of clusters which would read on determining number of clusters; See also Fig 4 which has clusters and optimization to identify annular conditions];
applying, for each time interval included in the plurality of different time intervals and using the number of clusters, an unsupervised machine-learning model to the plurality of ultrasonic waveforms for clustering, using the clustering operation the set of attributes of each ultrasonic waveform in the time interval into a set of clusters[0049, 0052 0054, Claim 1 has grouping/clustering using an unsupervised machine learning system for clustering], wherein each cluster of the set of clusters represents a different annular condition of a set of annular conditions possible in the wellbore[0049, 0052-0054, Claim 1 has grouping/clustering using an unsupervised machine learning system for clustering; Abstract, 0033 also has cement condition in the annulus being characterized meaning its an annular condition; 0058 and fig 4 also has clustering and cement condition in annulus]
outputting the set of clusters for categorizing the ultrasonic waveform data for distinguishing between channels and microannuli in the wellbore based on the set of annular conditions. [0033, 0037, Claim 1 has outputting the clusters which show wellbore properties such as cement condition and well integrity which relates to channels in the wellbore; Abstract, 0033 also has cement condition in the annulus being characterized meaning it's an annular condition; 0058 and fig 4 also has clustering and cement condition in annulus]
and based on updated cluster assignments in the set of clusters for identifying changes in the set of annular conditions over the plurality of different time intervals, determining a first annular condition of the set of annular conditions that is a persistent channel and a second annular condition of the set of annular conditions that is a transient microannuli in the wellbore[0033, 0037, Claim 1 has outputting the clusters which show wellbore properties such as cement condition and well integrity which relates to channels in the wellbore; Abstract, 0033 also has cement condition in the annulus being characterized meaning it's an annular condition; 0058 and fig 4 also has clustering and cement condition in annulus, 0047, 0051 and Claim 9 have data over time meaning monitoring changes over time ]
While Zhu implies, the data is ultrasonic, Kalyanraman teaches receiving ultrasonic waveform data about a wellbore using an arrangement of transducers positionable in the wellbore[Abstract, Fig 1; Claim 1], the ultrasonic waveform data including a plurality of ultrasonic waveforms [ Abstract, Fig 1; Claim 1]; the transducers tilted in a pitch-catch arrangement to cause flexural waves to be generated in the wellbore[Col 5; Lines 30-40 has pitch catch arrangement and flexural waves];.....and outputting the set of clusters for categorizing the ultrasonic waveform data for distinguishing between channels and microannuli in the wellbore based on the set of annular conditions. [Col 7, Lines 35-40; Col 8, Line 55- Col 9, Line 5; Fig 5, 6, Col 10 Lines 30-35 and 50-55 all have distinguishing channels and microannuli based on the acoustics].
Birchak teaches dividing the time window into one or more segments 19; Lines 35-40 and Claims 26, 28, 33 and 34 have signals in various time windows for processing received data is divided into parts for processing];
combining amplitudes and phases of the primary flexural wave of each segment of the one or more segments[ 14; Lines 45-60 have sum of signals and amplitudes and phases];
It would have been obvious to one of ordinary skill in the art before the filing date to have modified the sonic waves in Zhu to use ultrasonic waves as ultrasonic tools are well known in the art and to use the pitch catch arrangement and flexural waves of Kalyanraman with the various segments and time windows and combined amplitude and phases of Birchak to distinguish between channels and microannuli to get better wellbore data by processing the ultrasonic waveform data.
Moreover, it would have been obvious to one having ordinary skill in the art before the filing date to have used various known methods to determine an optimum number of clusters, attributes and segments in machine learning, since it has been held that where routine testing and general experimental conditions are present, discovering the optimum values or workable ranges until the desired effect is achieved involves only routine skill in the art. See, Inre Aller, 105 USPQ 233.
Regarding claims 2, 9 and 16, Zhu, as modified, teaches wherein the pitch- catch arrangement includes at least one source transducer and at least one receiver transducer, and wherein the ultrasonic waveform data further includes pulse echo arrangement data for use in the clustering. [Fig 1, 2 show source and receivers and 0033 - 0037, 0050 has various received frequencies in response to transmissions].
Regarding claims 3 and 10, Zhu, as modified, teaches wherein the operation of applying the unsupervised machine-learning model includes selecting at least one attribute from the set of attributes of each ultrasonic waveform for use in clustering the ultrasonic waveform data into the set of clusters. [0049, 0052-0054 and claim 1 has sonic data and clustering based on attributes]
Regarding claim 4, Zhu, as modifed, teaches wherein the operation of determining the number of clusters comprises performing Silhouette analysis to determine the number of clusters, [0053, 0054, 0057, 0100 has k-means clustering and optimization with reduction in cluster sum of squares in 0100; 0084 has optimization method; 0049, 0053 -0057 has choosing number of clusters which would read on determining number of clusters] wherein the operation of determining the number of clusters includes determining a reduction of sum of square among the set of attributes. [0053, 0054, 0057, 0100 has k-means clustering and optimization with reduction in cluster sum of squares in 0100; 0084 has optimization method; 0049, 0053 -0057 has choosing number of clusters which would read on determining number of clusters]
Moreover, it would have been obvious to one having ordinary skill in the art before the filing date to have used various mathematical known methods to optimize data in machine learning, since it has been held that where routine testing and general experimental conditions are present, discovering the optimum values or workable ranges until the desired effect is achieved involves only routine skill in the art. See, In re Aller, 105 USPQ 233. This is further evidenced by Ranganathan (US 20230204424 Al) at 0030, 0054, 0079-0080 which specifically mentions Silhouette.
Regarding claims 6 and 13, Zhu, as modified, teaches wherein the operation of applying the unsupervised machine-learning model includes applying a clustering algorithm that includes K-means clustering, mean-shift clustering, agglomerative hierarchical Clustering, or fuzzy clustering. [0054,has k-means clustering]
Regarding claims 7, 14 and 20, Zhu, as modified, teaches wherein the unsupervised machine -learning model is a first machine-learning model, and wherein the operations further comprise training a second machine-learning model by using the set of clusters as labeled training data. [0041 -0042, 0080, 0084 has machine learning and training where the training of the auto encoder and applying it to the data and training neural network which reads on the claim]
Regarding claim 11, Zhu, as modified, teaches wherein determining the number of clusters includes using the Elbow method to determine the number of clusters. [(0053, 0054,0057, 0100 has k-means clustering and optimization with reduction in cluster sum of squares in 0100; 0084 has optimization method; 0049, 0053-0057 has choosing number of clusters which would read on determining number of clusters]
Moreover, it would have been obvious to one having ordinary skill in the art before the filing date to have used various mathematical known methods to optimize data in machine learning, since it has been held that where routine testing and general experimental conditions are present, discovering the optimum values or workable ranges until the desired effect is achieved involves only routine skill in the art. See, Inre Aller, 105 USPQ 233. This is further evidenced by Ranganathan (US 20230204424 Al) at0030, 0054,0079-0080 which specifically mentions Elbow.
Regarding claims 12 and 18, Zhu, as modified, teaches wherein the operation of determining the number of clusters includes determining a reduction of sum of square among the set of attributes. [0053, 0054, 0057, 0100 has k-means clustering and optimization with reduction in cluster sum of squares in 0100; 0084 has optimization method; 0049, 0053 -0057 has choosing number of clusters which would read on determining number of clusters]
Moreover, it would have been obvious to one having ordinary skill in the art before the filing date to have used various mathematical known methods to optimize data in machine learning, since it has been held that where routine testing and general experimental conditions are present, discovering the optimum values or workable ranges until the desired effect is achieved involves only routine skill in the art. See, Inre Aller, 105 USPQ 233. This is further evidenced by Ranganathan (US 20230204424 Al) at0030, 0054,0079-0080 which specifically mentions Elbow, Silhouette and Davis-Bouldin.
Regarding claim 23, Zhu, as modified, teaches wherein the operation of determining the number of annular conditions is performed before the number of clusters is determined, wherein the operation of determining the number of annular conditions further comprises determining the number of annular conditions from a list of possible annular conditions, and wherein the number of annular conditions has fewer annular conditions than the list of possible annular conditions. [Abstract, 0037, 0057-0058 has conditions such as hard or soft or presence of water, 0057 also has selection of number of clusters as well as objective function being defined and used to determine number of clusters]
Moreover the selection of which conditions to search for, namely selection of parameters would be claimed invention would have been obvious matter of design choice and it has been held that where routine testing and general experimental conditions are present, discovering the optimum or workable ranges until the desired effect is achieved involves only routine skill in the art. See, In re Aller, 105 USPQ 233. Moreover, Applicant should note that nothing of record, nor known in the art, suggests that using the specific claimed range or value yields any previously unexpected results.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over over Zhu (US 2021/0270127 A1) in view of Kalyanraman (10,539,699 B2) and (Birchak US 5,924,499 A) as applied to claim 1 above, and further in view of Froelich (GB 2,399,411 A).
Regarding claim 21, Zhu, does not explicitly teach wherein the pitch-catch arrangement of transducers involves an angle between each transducer of the arrangement of transducers and a well tool positionable in the wellbore, and wherein the angle is non -perpendicular.
Froelich teaches wherein the pitch-catch arrangement of transducers involves an angle between each transducer of the arrangement of transducers and a well tool positionable in the wellbore, and wherein the angle is non-perpendicular. [Abstract; Figs 2, 4, 6A, 6B, 7 have transducers at angles which are not perpendicular which are comparable to applicants fig 1]
It would have been obvious to one of ordinary skill in the art before the filing date to have modified the transducers Zhu to have the pitch catch arrangement of transducers at angles as shown by Froelich in order to avoid unwanted echo signals [Abstract].
Claim 24 is rejected under 35 U.S.C. 103 as being unpatentable over over Zhu (US 2021/0270127 A1) in view of Kalyanraman (10,539,699 B2) and (Birchak US 5,924,499 A) as applied to claim 1 above, and further in view of Malmberg (US 20230039040 A1 ) or Walters (US 20180329113 A1).
Regarding claim 24, Zhu teaches wherein the operation of generating the set of attributes comprises generating the set of attributes for each ultrasonic waveform of the plurality of ultrasonic waveforms….., and wherein the operations further comprise[0051 has processing of data to generate attributes such as slowness over time/frequency/space; 0047 has time windows to of returns being processed]:
receiving the additional sensor data from a mud pulse sensor and an electromagnetic sensor associated with the wellbore[0060 has electromagnetic and fig 2 shows tool in wellbore which would be filled with mud];
simulating, for each cluster of the set of clusters in each time interval of the plurality of different time intervals, acoustic propagation based on the ultrasonic waveform data using a ….. to test whether a portion of the ultrasonic waveform data in the cluster is output if a corresponding annular condition of the cluster is present in the wellbore[0033, 0037, Claim 1 has outputting the clusters which show wellbore properties such as cement condition and well integrity which relates to channels in the wellbore; Abstract, 0033 also has cement condition in the annulus being characterized meaning it's an annular condition; 0058 and fig 4 also has clustering and cement condition in annulus, 0047, 0051 and Claim 9 have data over time meaning monitoring changes over time]; …
Zhu does not explicitly teach by combining the ultrasonic waveform data with additional sensor data to form fused multi-modal data….. physics based forward model ….. validating that a subset of clusters of the set of clusters is consistent with output from the physics-based forward model
Malmberg teaches that by combining the ultrasonic waveform data with additional sensor data to form fused multi-modal data[0012 has multi modality for cement analysis]
Walters teaches physics based forward model[0024 and 0048 has physics based model]…..and validating that a subset of clusters of the set of clusters is consistent with output from the physics-based forward model[0024 and 0069 has data validation using physics model]
It would have been obvious to one of ordinary skill in the art before the filing date to have modified the cement sensor in Zhu with the sensor fusion and multi modality of Malmberg and the physics based model and validation of Walters in order to get better data and validate the cluster output.
Response to Arguments
Applicant's arguments filed 03/26/2026 have been fully considered but they are not persuasive.
Applicant is reading the prior art overly narrowly. With regards to Applicants Arguments Section A. It is pointed out in the cited art that a number of clusters are chosen or eliminated and the cement status is being determined meaning the number of clusters is determinable.
With regards to Applicants Arguments Section B. It is pointed out the fact that Zhu has time windows means that time windows are being set and any adjustment of time windows by a person of ordinary skill in the art such as routine optimization would result in the time window being before an end of data and read on the claim.
With regards to Applicants Arguments Section C. It is pointed out in the present rejection that Zhu processing over time. Moreover a person of ordinary skill in the art who takes measurements at more than once would invariably cause over time monitoring and updated data and thus read on the claim.
Applicant's remaining arguments amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references. Rejections are maintained – and no allowable subject matter can be identified at this time.
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.
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/VIKAS ATMAKURI/Examiner, Art Unit 3645
/JAMES R HULKA/Primary Examiner, Art Unit 3645
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