DETAILED ACTION
This action is responsive to the following communications: Original Application filed on December 20, 2023, and the Applicant Response to Pre-Exam Formalities Notice filed on February 14, 2024. All references to this application refer to the U.S. Patent Application Publication No. 2024/0211806 A1.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 are pending in this case. Claims 1, 19, and 20 are the independent claims. Claims 1-20 are rejected.
Priority
The present application claims the benefit of U.S. Provisional Patent Application No. 63/476,648, filed on December 22, 2022.
Drawings
The replacement drawings were received on February 14, 2024. They are acceptable.
Claim Interpretation - 35 USC § 101
The Examiner has reviewed the claims for compliance with 35 U.S.C. 101, specifically whether the claims recite a judicial exception without significantly more.
The present independent claims are of similar structure to Example 47 claims 1 and 3 (eligible), and distinguishable from Example 47 claim 2 (ineligible). Specifically, the claims do not recite particular mathematical steps (such as backpropagation or gradient descent), but instead merely describe receiving data sets, training a linear regression model using the input data sets, and using the trained regression model to compare portions of the data sets to determine variation. Accordingly, claims 1, 19, and 20 are eligible.
The dependent claims similarly do not recite judicial exceptions in view of Example 47, and therefore are similarly eligible.
Accordingly, claims 1-20 recite eligible subject matter under 35 U.S.C. 101.
Examiner’s Note
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.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the Examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicants are advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the Examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-6, 11-16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 2020/0322703 A1, filed by Bures et al., on March 30, 2020, and published on October 8, 2020 (hereinafter Bures), in view of Non-Patent Literature reference entitled “Silhouettes: a graphical aid to the interpretation and validation of cluster analysis,” by Rousseeuw, Peter J., published in the Journal of Computational and Applied Mathematics 20 (1987), pgs. 53-65 (hereinafter Rousseeuw).
With respect to independent claim 1, Bures discloses a method comprising:
Receiving a first set of time series sensor data of a region and a second set of time series sensor data of the region; Bures discloses receiving first and second sets of timer-series sensor data associated with a region (see Bures, paragraphs 0019 [system can monitor environmental, electrical, or operational activity at indoor or outdoor facilities using sensors that collect data over time], 0022 [received data can be utilized for trend, correlation, priority of conditions, determine entropy or quality of data, etc.; the data can be used to train ML models which can be used to predict/infer conditions, generate alerts, etc.], 0040 [data collection can be controlled to determine which sensors collect which data, how much data, types of data, etc.], 0043 [operational mode determines frequency of data collection and time windows for data collection, including time between data collection], 0204 [time-series data can be discrete or continuous, and can be of video, image, audio, text, or numeric], 0211-0212 [timestamps are used to associate sensor data with time/location of detection, and single or multiple measurements may be collected from one or more sensors; data collection can be synchronous or asynchronous], and 0268 [sensor data can be associated with particular locations within a facility]).
Training a regression model using the first set and the second set to generate a trained regression model; Bures discloses using the received data to train a regression model (see Bures, paragraphs 0293-0296 [describing the use of measurement data to train ML models, including regression models, in order to perform inference and predictive operations based on sensor data, including training using first and second subsets of measurement data], 0300-0301 [models can be trained to generate predictions over different time periods (long vs. short) and to determine significance of measurements], and 0305 [multiple models can be trained utilizing different measurements and measurement types]).
Transforming at least a portion of the first set to a comparison space, using the trained regression model, to generate a comparison set; Bures discloses transforming at least a portion of the measurement data using the trained ML model to generate a comparison set (see Bures, paragraphs 0035 [measurements can be pre-processed], 0203 [measurement data is normalized foe ease of consumability], 0210 [measurement values are normalized using normalization functions, including linear functions, such that all values can be compared within tolerances and ranges], 0261 [normalization is used to normalize values from different sensors], and 0474 [measurements are normalized via linear functions]; see also, Bures, paragraphs 0204, 0293-0296, 0300-0301, and 0305, described supra).
Although Bures teaches calculating statistical measures such as mean, median, standard deviation, variance, etc. (see Bures, paragraph 0281 [using statistical analysis with thresholds to determine measurement quality], Bures fails to expressly disclose comparing at least a portion of the second set to the comparison set to determine variation between the first set and the second set with respect to the region.
However, Rousseeuw teaches using silhouettes in order to visualize cluster analysis by comparing data from different clusters to determine variation (see Rousseeuw, page 55 [using clusters to determine similarity or difference between clustered data based on Euclidean distances by calculating average dissimilarity of an object to all objects within cluster A and average dissimilarity of the object to all objects in cluster B (see page 57 for similarity calculations)] and page 56 [defining the three primary situations in object clustering: s(i) is closest to 1 (well-clustered), s(i) = 0 (clustering is unclear/intermediate), and s(i) closest to -1 (object is misclassified)]).
Accordingly, it would have been obvious to one of ordinary skill in the art, having the teachings of Bures and Rousseeuw before him before the effective filing date of the claimed invention, to modify the method of Bures to incorporate comparing data from different data sets as taught by Rousseeuw. One would have been motivated to make such a combination because this makes it easier to determine similarities or differences of clustered data, as taught by Rousseeuw (see Rousseeuw, page 55 [“At this stage we are left with many questions. Are the clusters of a high quality (that is, are the ‘within’ dissimilarities small when compared to the ‘between’ dissimilarities)? Which objects appear to be well-classified, which ones are misclassified, and which ones lie in between clusters? What is the overall structure of the data like? Can we obtain an idea about the number of ‘natural’ clusters that are really present? These questions are difficult, and we feel that the existing displays answer them only partially. It is hoped that the silhouettes introduced in the next section will provide the user with additional guidance.]).
With respect to dependent claim 2, Bures, as modified by Rousseeuw, teaches the method of claim 1, as described above.
Bures further teaches the method wherein the training comprises generating a first linear approximation set using the first set, generating a second linear approximation set using the second set, determining a metric using the first linear approximation set and the second linear approximation set, and clustering the metric to identify a first cluster associated with the first set and a second cluster associated with the second set.
Bures further teaches generating linear approximations of each measurement set using normalization, and applying a clustering algorithm and metric to identify clusters (see Bures, paragraphs 0035, 0203-0204, 0210, 0261, 0293-0296, 0300-0301, 0305, and 0474, described supra, claim 1).
With respect to dependent claim 3, Bures, as modified by Rousseeuw, teaches the method of claim 2, as described above.
Bures further teaches the method wherein the training comprises using time series data members of the first set from the first cluster and time series data members of the second set from the second cluster.
Bures further teaches using time-series data from the first set from the first cluster and time-series data from the second set from the second cluster (see Bures, paragraphs 0035, 0203-0204, 0210, 0261, 0293-0296, 0300-0301, 0305, and 0474, described supra, claim 1).
With respect to dependent claim 4, Bures, as modified by Rousseeuw, teaches the method of claim 2, as described above.
Rousseeuw further teaches the method comprising performing a silhouette analysis on the clustering to assess distance between the first cluster and the second cluster.
Rousseeuw further teaches using silhouette analysis on clustering to assess distance between clusters (see Rousseeuw, Figs. 1-7; see also, Rousseeuw, pages 60-62 [describing the examples of Figs. 4-7 using different k values for clustering and presenting the silhouette analysis for those clusters; clustering based on Euclidean distances]; see also, Rousseeuw, pages 55-56, described supra, claim 1).
With respect to dependent claim 5, Bures, as modified by Rousseeuw, teaches the method of claim 2, as described above.
Rousseeuw further teaches the method wherein the clustering comprises k-means clustering.
Rousseeuw further teaches clustering using K-means and K-median approaches (see Rousseeuw, page 53 [introduction, background of clustering algorithms] and page 54 [describing the clustering of Table 1 data using both K-median and K-means algorithms]; see also, Rousseeuw, pages 60-62, described supra, claim 4).
With respect to dependent claim 6, Bures, as modified by Rousseeuw, teaches the method of claim 5, as described above.
Rousseeuw further teaches the method wherein a k parameter of the k-means clustering is equal to two or three.
Rousseeuw further teaches k-means clustering using k parameters of at least 2 (and up to 12) (see Rousseeuw, page 59 [describing methodologies for choosing the “best” value of k; using the Table 1 data, and analyzing all values of k from 2 to 12, determines that k=3 is the best value for that data]; see also, Rousseeuw, pages 53-54, described supra, claim 5; see also, Rousseeuw, pages 60-62, described supra, claim 4; see also, Rousseeuw, pages 55-56, described supra, claim 1).
With respect to dependent claim 11, Bures, as modified by Rousseeuw, teaches the method of claim 1, as described above.
Bures further teaches the method wherein the first set and the second set are acquired by the same sensor.
Bures further teaches the measurement data (first and second sets) are acquired by the same sensor (see Bures, Fig. 2; see also, Bures, paragraphs 0024 [describing the main components of a multi-sensor unit], 0034 [multi-sensor units can comprise a single sensor or multiple sensor devices; the sensors can collect measurements from different directions, locations, or concerning different features, or alternatively, each sensor can collect a distinct type of measurement], 0054 [measurements can be collected by a single sensor at a single time, or multiple sensors at a single time, or multiple sensors at multiple times], 0061 [describing an example of a single sensor collecting data once per millisecond for a second at each epoch, therefore the single sensor collects one thousand measurements in one second intervals as directed], and 0065-0066 [describing the time window for measurement collection]; see also, Bures, paragraphs 0040, 0043, and 0211-0212, described supra, claim 1).
With respect to dependent claim 12, Bures, as modified by Rousseeuw, teaches the method of claim 11, as described above.
Bures further teaches the method wherein the first set is acquired over a first time period and wherein the second set is acquired over a second time period.
Bures further teaches the first set is acquired over a first time period and the second set is acquired over a second time period (see Bures, paragraphs 0054, 0061, and 0065-0066, described supra, claim 11; see also, Bures, paragraphs 0040, 0043, and 0211-0212, described supra, claim 1).
With respect to dependent claim 13, Bures, as modified by Rousseeuw, teaches the method of claim 1, as described above.
Bures further teaches the method wherein the first set and the second set are acquired by different sensors.
Bures further teaches the first and second sets of measurement data are acquired by different sensors (see Bures, paragraphs 0054, 0061, and 0065-0066, described supra, claim 11; see also, Bures, paragraphs 0040, 0043, and 0211-0212, described supra, claim 1).
With respect to dependent claim 14, Bures, as modified by Rousseeuw, teaches the method of claim 13, as described above.
Bures further teaches the method wherein the different sensors are part of a common tool.
Bures further teaches a multi-sensor device (see Bures, Fig. 2; see also, Bures, paragraphs 0024 and 0034, described supra, claim 11).
With respect to dependent claim 15, Bures, as modified by Rousseeuw, teaches the method of claim 1, as described above.
Bures further teaches the method comprising storing an indicator of the variation in a database in association with a sensor.
Bures further teaches storing indicators of variance in measurement data in a database (see Bures, Figs. 5-7; see also, Bures, paragraphs 0199 [describing the database system, comprising a plurality of databases for storing various data captured and used by the monitoring system, including measurements, context data, functions, features, etc.], 0220 [data relating to operational efficiency, productivity, quality, performance, equipment maintenance, metrics associated with success or failure of equipment operation, etc.], and 0276 [describing the use of thresholds and conditional criteria used to determine whether requirements are met/unmet and triggering alerts as necessary]).
With respect to dependent claim 16, Bures, as modified by Rousseeuw, teaches the method of claim 15, as described above.
Bures further teaches the method comprising, based on the indicator, issuing a service call for the sensor.
Bures further teaches determining that a condition of interest (e.g., malfunction) has occurred and issuing an alert for maintenance or repair (see Bures, paragraphs 0318-0320 [describing detection functionality which ascertains that equipment is not performing as expected (e.g., malfunction), and trigger issuance of alerts with mitigation instructions to correct the malfunction or replace the sensor]).
With respect to dependent claim 18, Bures, as modified by Rousseeuw, teaches the method of claim 1, as described above.
Bures further teaches the method wherein the first set and the second set comprise time series acoustic data.
Bures further teaches the time-series data comprises acoustic data (see Bures, paragraph 0148 [time-series data including acoustic data (e.g., sound, vibration, noise, etc.)]).
Independent claim 19 recites a system comprising: one or more processors; memory accessible to at least one of the one or more processors; processor-executable instructions stored in the memory and executable to instruct the system to perform the method of independent claim 1. Accordingly, independent claim 19 is rejected under the same rationales used to reject independent claim 1, which are incorporated herein.
Independent claim 20 recites one or more computer-readable storage media comprising processor-executable instructions to instruct a computing system to perform the method of independent claim 1. Accordingly, independent claim 20 is rejected under the same rationales used to reject independent claim 1, which are incorporated herein.
Claims 7-9 are rejected under 35 U.S.C. 103 as being unpatentable over Bures, in view of Rousseeuw, further in view of Non-Patent Literature reference entitled “Graphical Tools for Increasing the Effectiveness of Gage Repeatability and Reproducibility Analysis,” by Plura et al., published Processes on December 20, 2022 (hereinafter Plura).
With respect to dependent claim 7, Bures, as modified by Rousseeuw, teaches the method of claim 1, as described above.
Bures and Rousseeuw fail to further teach the method wherein the variation indicates repeatability for one or more sensors.
However, Plura teaches using repeatability and reproducibility (R&R) analysis to indicate repeatability for sensors (see Plura, page 2 [listing the main properties of measurement system analysis used to ascertain acceptance of tools, readings, and systems, including repeatability, reproducibility, and consistency; repeatability, reproducibility, and consistency are considered ‘characteristics of variability’; consistency is defined as a degree of change over time, while repeatability is defined as independent measurements obtained by a single operators using a single method with the same sensor at the same location], page 3 [reproducibility is defined as variability of repeated measurements taken under different conditions, typically by multiple operators, or different equipment or environments], and pages 4-5 [describing the ANOVA technique (analysis of variance)]).
Accordingly, it would have been obvious to one of ordinary skill in the art, having the teachings of Bures, Rousseeuw, and Plura before him before the effective filing date of the claimed invention, to modify the method of Bures, as modified by Rousseeuw, to incorporate using Gage analysis to indicate repeatability as taught by Plura. One would have been motivated to make such a combination to improve application of R&R analysis, as taught by Plura (see Plura, page 2 [“The article focuses on improving the methodological approach to the analysis of the repeatability and reproducibility of the measurement system, where insufficient attention is paid to the graphical tools of the analysis. Modified as well as completely new graphical tools of analysis are suggested, which will better reveal the effect of the various factors involved in the variability of the measurement system.”]).
With respect to dependent claim 8, Bures, as modified by Rousseeuw, teaches the method of claim 1, as described above.
Bures and Rousseeuw fail to further teach the method wherein the variation indicates reproducibility for one or more sensors.
However, Plura teaches using R&R analysis to indicate reproducibility for sensors (see Plura, page 2, page 3, and pages 4-5, described supra, claim 7).
Accordingly, it would have been obvious to one of ordinary skill in the art, having the teachings of Bures, Rousseeuw, and Plura before him before the effective filing date of the claimed invention, to modify the method of Bures, as modified by Rousseeuw, to incorporate using Gage analysis to indicate reproducibility as taught by Plura. One would have been motivated to make such a combination to improve application of R&R analysis, as taught by Plura (see Plura, page 2, described supra, claim 7).
With respect to dependent claim 9, Bures, as modified by Rousseeuw, teaches the method of claim 1, as described above.
Bures and Rousseeuw fail to further teach the method wherein the variation indicates an inconsistency for one or more sensors.
However, Plura teaches using R&R analysis to indicate consistency (or inconsistency) for sensors (see Plura, page 2, page 3, and pages 4-5, described supra, claim 7).
Accordingly, it would have been obvious to one of ordinary skill in the art, having the teachings of Bures, Rousseeuw, and Plura before him before the effective filing date of the claimed invention, to modify the method of Bures, as modified by Rousseeuw, to incorporate using Gage analysis to indicate inconsistency as taught by Plura. One would have been motivated to make such a combination to improve application of R&R analysis, as taught by Plura (see Plura, page 2, described supra, claim 7).
Claims 10 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Bures, in view of Rousseeuw, further in view of Non-Patent Literature reference entitled “Cement bond quality evaluation based on acoustic variable density logging,” by Tang et al., published in Research Institute of Petroleum Exploration and Development, Vol. 43, Issue 3, February 25, 2016, pgs. 514-521 (hereinafter Tang).
With respect to dependent claim 10, Bures, as modified by Rousseeuw, teaches the method of claim 1, as described above.
Bures and Rousseeuw fail to further teach the method wherein the region comprises a borehole region of a borehole in a subsurface geologic environment.
However, Tang teaches using sensors to analyze a region that comprises a borehole region in a subsurface geologic environment, such as for oil or gas drilling (see Tang, page 514 [describing the use of borehole acoustic field study, specifically for determining cement bonding of drill shafts] and pages 516-517 [describing the comparison of simulation of wells with actual readings]).
Accordingly, it would have been obvious to one of ordinary skill in the art, having the teachings of Bures, Rousseeuw, and Tang before him before the effective filing date of the claimed invention, to modify the method of Bures, as modified by Rousseeuw, to incorporate analyzing subsurface geologic environments of boreholes, with particular emphasis on cement bonding acoustic analysis, as taught by Tang. One would have been motivated to make such a combination to improve evaluation of cement bonding quality in subsurface boreholes, as taught by Tang (see Tang, pages 514-515 [“According to the actual size of oil and gas wells, the non-axisymmetric acoustic field has been numerically simulated with 2.5-D finite differential method and then compared with the casing waveform acquired by acoustic variable density logging tool in the calibration wells to evaluate the reliability of the numerical simulation method and analyze the effect of cement channeling angle on the acoustic variable density logging; and the axisymmetric acoustic field has been calculated with real axis integral method to analyze the effect of the annulus width of bond interface II on the casing arrival waveform and formation arrival waveform; based on the analysis of the effect of cement density and cement channeling angle on the CBL/VDL logging quality, a new improved
cement bond quality evaluation method with CBL/VDL data has been advanced in this study.”]).
With respect to dependent claim 17, Bures, as modified by Rousseeuw, teaches the method of claim 1, as described above.
Bures and Rousseeuw fail to further teach the method wherein the first set and the second set comprise time series cement bond logging data.
However, Tang teaches using acoustic sensors to analyze cement bonding quality within boreholes, such as for oil or gas drilling, using time-series acoustic data (see Tang, page 518 [describing Fig. 6, comparing acoustic waves over time] and pages 519 [describing the new evaluation standard determined by analysis of the acoustic waveform (e.g., time-series) data]; see also, Tang, pages 514 and 516-517, described supra, claim 10).
Accordingly, it would have been obvious to one of ordinary skill in the art, having the teachings of Bures, Rousseeuw, and Tang before him before the effective filing date of the claimed invention, to modify the method of Bures, as modified by Rousseeuw, to incorporate cement bonding acoustic analysis via cement bond logging time-series data, as taught by Tang. One would have been motivated to make such a combination to improve evaluation of cement bonding quality in subsurface boreholes, as taught by Tang (see Tang, pages 514-515, described supra, claim 10).
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicants’ disclosure. See PTO-892.
It is noted that any citation to specific pages, columns, figures, or lines in the prior art references any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331-33, 216 USPQ 1038-39 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)).
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to ERIC J. BYCER whose telephone number is (571) 270-3741. The Examiner can normally be reached Monday - Thursday 9am-6pm, and alternate Fridays 9am-5pm.
Examiner interviews are available via a variety of formats. See MPEP § 713.01. To schedule an interview, Applicants are encouraged to use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/InterviewPractice.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, MATT ELL can be reached on (571) 270-3264. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ERIC J. BYCER/
Primary Examiner
Art Unit 2141
Prosecution Insights