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 .
DETAILED ACTION
Status of the Application
The following is a non-Final Office Action.
In response to Examiner's communication of 1/27/2026, Applicant responded on 4/27/2026. Amended claim 1, 11, 18.
Claims 1-20 are pending in this application have been examined.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 4/27/2026 has been entered.
Response to Amendment
Applicant's amendments to claims 1, 11, 18 are not sufficient to overcome the 35 USC 101 rejections set forth in the previous action.
Applicant's amendments to claims 1, 11, 18 are sufficient to overcome the prior art rejections set forth in the previous action.
Response to Arguments – 35 USC § 101
Applicant’s arguments with respect to the rejections have been fully considered, but they are not persuasive.
Applicant submits, “…The present claims provide this "something more" that Desjardins and the Director describe. In particular, the pending claims do not merely evaluate anomaly data differently. Instead, they modify the architecture of a prognostic system by introducing a dataset-driven gating mechanism that alters the flow of anomaly information into downstream modeling, which is a patent-eligible technological improvement under the Director's guidance.…These limitations do not merely evaluate anomaly data differently. Instead, the claims modify the architecture of a prognostic pipeline by introducing a dataset-driven filtering stage that alters the flow of anomaly information into downstream modeling. In particular, the claimed irrelevance filter selectively removes anomaly alarms prior to their use in downstream processing based on cross-device, outcome-labeled historical pattern equivalence. This changes how anomaly information propagates through the system, as only anomaly alarms that are correlated with failure are permitted to influence subsequent modeling stages. The claims therefore define a structural modification to the information flow within the system, rather than a mere abstract evaluation of data. This is a change to how the information flows in the training pipeline. This is patent-eligible as explained in the Desjardins Memo and by the Director….this architectural change produces a concrete technological improvement. By removing anomaly alarms that correspond to patterns historically associated with operation without incident, the system prevents non-failure-correlated signals from influencing downstream prognostic computations. As a result, the statistical characteristics of the anomaly alarm stream are improved, and downstream models- such as remaining useful life models-operate on a refined input set that more accurately reflects failure-relevant behavior. As the specification puts it, "systematically and safely filtering anomaly alerts generated for individual utility system assets so that RUL-analysis operations are only performed for "relevant" signature patterns that are likely to be associated with incipient fault conditions." This improves the performance of the system as a whole, including reducing false indications of degradation and improving prognostic accuracy." For at least these reasons, the present claims integrate any alleged abstract idea into a patent-eligible practical application at Step 2A, prong two, and recite significantly more than the alleged abstract idea….This analysis improperly ignores the specific structural limitations previously recited, and now expanded upon in the amended claims. As amended, the irrelevance filter is not a mental process or mathematical formula. It is a specific, machine- implemented mechanism that operates based on a stored dataset involving: historical time-series signal patterns, associated operational outcomes, and data collected across a plurality of similar electronic devices….The filter performs dataset-driven pattern matching and outcome-based discrimination to remove anomaly alarms prior to downstream processing. This is not a generalized idea of "filtering," but a specific computational operation tied to defined data structures and system behavior…The Office's position effectively treats any claimed improvement as abstract simply because it can be described at a high level. This approach is inconsistent with controlling guidance, which requires evaluating the claim based on its actual limitations, not an oversimplified characterization…This conclusion remains contrary to the August Memo, which expressly prohibits expanding the mental process grouping to encompass limitations that cannot practically be performed in the human mind. The Office's response does not engage this standard. Instead, it repeats a conclusory assertion that the claimed operations "can include" mental performance, without addressing whether such performance is practically possible under the claim as a whole…These limitations of the original independent claims operate on continuous sensor telemetry and vast collections of historical alerts and associated outcomes. Such operations cannot practically be performed in the human mind, nor with pen and paper. This is particularly so for performance as claimed in an ongoing surveillance mode. The Office's assertion that the entire claimed method "can include a human using their mind and pen and paper" is conclusory and unsupported, and reflects exactly the type of improper expansion of the mental process category that the August Memo prohibits. And, the independent claims as currently amended recite that the irrelevance filter operates based on a stored dataset comprising historical time-series signal patterns and associated operational outcomes for a plurality of similar electronic devices, and removes anomaly alarms based on (i) lack of correlation with failure and (ii) matching to patterns associated with operation without incident…Such operations as recited in the claims as currently amended cannot practically be performed in the human mind, nor with pen and paper-particularly in the claimed surveillance context involving continuous telemetry and ongoing evaluation…This analysis fails to follow the August Memo's explicit instruction to distinguish claims that actually recite a judicial exception from claims that merely involve one. As the August Memo explains, a claim recites a mathematical concept only when it sets forth or describes a mathematical relationship, calculation, formula, or equation. Claims 1, 11, and 18 do not recite any mathematical formula, equation, algorithm, or calculation-by name or otherwise, as is plain on the face of the claims. Instead, the claims recite an applied prognostic surveillance workflow that uses an irrelevance filter to suppress false alarms based on historically observed device outcomes (failure versus non-failure)…..The Office's reliance on the specification's discussion of SPRT does not alter this conclusion. The independent claims do not recite SPRT as a mathematical formula; they recite the use of anomaly alarms and a subsequent irrelevance filter that operates on those alarms using a dataset of historical patterns and outcomes. At most, the claims involve statistical techniques as part of a larger technological process. This is insufficient to establish that the claims recite a mathematical concept under Step 2A Prong One. Accordingly, the independent claims do not recite a mathematical concept and cannot be found to recite an abstract idea at Step 2A, Prong One…The specification's reference to biomimicry is merely descriptive and explanatory. It does not define the claimed mechanism as a mental process. The claims here do not recite human cognition. Instead, the claims recite machine operations that improve machine-implemented prognostic anomaly detection with an irrelevance filter, which cannot be reduced to human mental activity. The operations include, for example, processing time-series sensor data, operating on stored datasets spanning multiple devices, and performing pattern matching and correlation analysis to control system behavior as recited in the claims. Analogy does not define claim scope. Claim language does. The Office's reliance on analogy rather than claim language is misplaced… the claims are directed to a technological improvement of providing an irrelevance filter in a technical field of prognostic anomaly detection... This improvement serves to integrate any alleged abstract idea into a practical application at step 2A, prong two….” The Examiner respectfully disagrees.
Examiner notes, unlike Desjardins, August Memo, by Applicant’s own admission in Applicant’s specification and Applicant’s remarks, the claims, as a whole, indeed recite and direct to, …field of prognostic anomaly detection…an applied prognostic surveillance workflow that uses an irrelevance filter to suppress false alarms based on historically observed device outcomes (failure versus non-failure)…irrelevance filter that removes anomaly alarms based on empirical outcome-based correlations and prior non-incident pattern matching…the statistical characteristics of the anomaly alarm stream are improved, and downstream models- such as remaining useful life models-operate on a refined input set that more accurately reflects failure-relevant behavior…reducing false indications of degradation and improving prognostic accuracy… specification's discussion of SPRT…. the claims involve statistical techniques….processing time-series…operating on stored datasets…performing pattern matching and correlation analysis…, which is a problem directed to a mental process (i.e. human processing time-series…operating on stored datasets…performing pattern matching and correlation analysis, human observing and evaluating electronic devices’ operation patterns, humans judging anomalous operating pattern with statistical analysis and filtering out anomalies and irrelevant anomaly with statistical SPRT technique, humans notifying humans remaining useful life of electronic devices after humans evaluating with statical analysis) and mathematical concepts (i.e. humans judging anomalous operating pattern with statistical analysis and filtering out anomalies and irrelevant anomaly with statistical SPRT technique, humans notifying humans remaining useful life of electronic devices after humans evaluating with statical analysis), as established in Step 2A Prong 1. This problem does not specifically arise in the realm of computer technology, but rather, this problem existed and was addressed long before the advent of computers. Thus, the claims do not recite a technical improvement to a technical problem or necessarily roots in computing technologies. Pursuant to the broadest reasonable interpretation, as an ordered combination, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea, and thus, are no more than applying the abstract idea with generic computer components, i.e. computer and sensors. Further, these additional elements generally link the abstract idea to a technical environment, namely the environment of a computer and sensors, performing extra solution activities. Therefore, as a whole, the additional elements do not integrate the abstract ideas into a practical application in Step 2A Prong 2 (apply it and general link) or amount to significantly more in Step 2B (apply it and wurc).
Applicant’s arguments hinge on “anomaly alarms”, “irrelevance filter ” element being an additional element beyond the identified abstract ideas. However, as claimed, under the broadest reasonable interpretation, Examiner is interpreting the “anomaly alarms”, “irrelevance filter” to be abstract elements that is part of and directed to the identified abstract idea and this element is addressed in Step 2A, Prong1.
Further, as per Berkheimer memo, according to Applicant’s own specifications, the “anomaly alarms”, “irrelevance filter” element is indeed an abstract element directed to a mental process and mathematical concepts.
[0007] In one embodiment, a method performed by the present system estimates a remaining useful life (RUL) of an electronic device is disclosed. For example, during a surveillance mode, the method comprises the following actions/functions. A set of time-series signals gathered from sensors in the electronic device are received while the electronic device is operating. Statistical changes are detected in the set of time-series signals that are deemed as anomalous signal patterns. A set of anomaly alarms are generated, wherein an anomaly alarm is generated for each of the anomalous signal patterns. An irrelevance filter is applied to the set of anomaly alarms to produce filtered anomaly alarms that do not include suspected false alarms, wherein the irrelevance filter removes anomaly alarms associated with one or more anomalous signal patterns that are not correlated with previous failures of similar electronic devices that are similar to the electronic device; wherein removing the suspected false alarms from the set of anomaly alarms, by the irrelevance filter, comprises removing a target anomaly alarm associated with an anomalous signal pattern when the anomalous signal pattern matches a similar signal pattern that was previously observed from the similar electrical devices that have operated without incident. A notification may be generated indicating an estimated remaining useful life and/or that the electronic device has a limited remaining useful life (e.g., device is near a failing point and/or should be replaced).
[0030]Hence, what is needed is an “irrelevance filter” that processes time-series signals for utility system assets that have been run to failure, and produces optimal weighting factors for an associated RUL methodology. Note that this is analogous to the functionality of a basal ganglia “filter” for a human brain, which receives large streams of neural “signals” associated with the five primary senses, and periodically “alerts” the human to patterns that have direct relevance to danger, subsistence, or propagation-of-species opportunities.
[0032]Our anomaly discovery process uses a systematic binary hypothesis technique called the “sequential probability ratio test” (SPRT) as an irrelevance filter for large volumes of time-series signals, and identifies small subsets of time-series signals that warrant further pattern-recognition analyses to facilitate anomaly detection. Hence, our new technique substantially reduces RUL-analysis costs by systematically and safely filtering anomaly alerts generated for individual utility system assets so that RUL-analysis operations are only performed for “relevant” signature patterns that are likely to be associated with incipient fault conditions.
[0073] Referring to FIG. 1, NLNP regression model 108 and difference module 112 work together to remove (filter) the dynamics in the signals X(t) so that the residual R(t) is a stationary random process when the system is in good condition. As the system ages or degrades due to a failure mechanism, the statistical properties of the residual change. This change is detected by SPRT module 116, which generates corresponding SPRT alarms 118.
[0082] Next, the system applies an irrelevance filter to the anomaly alarms (e.g., SPRT alarms) to produce a filtered anomaly alarms (e.g., SPRT alarms), wherein the irrelevance filter removes SPRT alarms for signals that are not correlated with previous failures of similar utility system assets (step 210).
[0083] The system then uses a logistic-regression model to compute an RUL-based risk index for the utility system asset based on tripping frequencies of the filtered SPRT alarms (step 212). If the risk index exceeds a risk-index threshold, the system generates a notification indicating that the electronic device has a limited remaining useful life (e.g., is near a predicted failing point) and should be replaced (step 214).
[0085] FIG. 4 presents a flow chart illustrating a process for training a logistic-regression model to predict an RUL for an asset and for configuring an associated irrelevance filter
[0086] The irrelevance filter is also configured to remove SPRT alarms (e.g., anomaly alarms) that are not relevant (step 416). SPRT alarms that are not relevant include alarms that occur in time intervals that are not near a failure time of the asset/device (e.g., a time beyond/outside the time threshold).
Examiner respectfully notes, by Applicant’s own admission in Applicant’s specification,
[0007] Statistical changes are detected in the set of time-series signals that are deemed as anomalous signal patterns. A set of anomaly alarms are generated, wherein an anomaly alarm is generated for each of the anomalous signal patterns.
[0030]Hence, what is needed is an “irrelevance filter” that processes time-series signals for utility system assets that have been run to failure, and produces optimal weighting factors for an associated RUL methodology. Note that this is analogous to the functionality of a basal ganglia “filter” for a human brain, which receives large streams of neural “signals” associated with the five primary senses, and periodically “alerts” the human to patterns that have direct relevance to danger, subsistence, or propagation-of-species opportunities. (i.e. mental process)
[0032]Our anomaly discovery process uses a systematic binary hypothesis technique called the “sequential probability ratio test” (SPRT) as an irrelevance filter for large volumes of time-series signals (i.e. mathematical concepts)
The “anomaly alarms”, “irrelevance filter” are indeed abstract elements directed to a mental process and mathematical concepts.
Furthermore, according to, https://web.archive.org/web/20090802201425/http://en.wikipedia.org/wiki/Sequential_probability_ratio_test, 8/2/2009, “The sequential probability ratio test (SPRT) is a specific sequential hypothesis test, developed by Abraham Wald.[1] Neyman and Pearson's 1933 result inspired Wald to reformulate it as a sequential analysis problem. The Neyman-Pearson lemma, by contrast, offers a rule of thumb for when the all the data is collected (and its likelihood ratio known). While originally developed for use in quality control studies in the realm of manufacturing, SPRT has been formulated for use in the computerized testing of human examinees as a termination criterion.” Thus, sequential probability ratio test (SPRT) is an abstract mathematical statistical method developed for the purpose of organizing human activities.
Even further, Applicant admits in Applicant’s Specification,
[0055] SPRT module 116 then performs a “detection operation” on the residuals 114 to detect anomalies and possibly to generate SPRT alarms 118. SPRT module uses the sequential probability ratio test (SPRT) proposed by Wald to detect subtle statistical changes in a stationary noisy sequence of observations at the earliest possible time. (See Wald, Abraham, June 1945, “Sequential Tests of Statistical Hypotheses,” Annals of Mathematical Statistics,16(2): 117–186.) For purposes of exposing the details of the SPRT, assume that the monitored process signal Y is normally distributed with mean zero and standard deviation σ (processes with nonzero mean μ can be transformed into a zero-meaned process by subtracting μ from each observation). Process signal Y is said to be degraded if the observations made on Y appear to be distributed about mean M with normal (Gaussian) distribution instead of mean zero, where M is a predetermined system disturbance magnitude.
Thus, Examiner’s interpretation and analysis of the argued element “irrelevance filter” being an abstract element is indeed correct and addressed in Step 2A Prong1.
Applicant’s specification clearly admits and only supports the “anomaly alarms”, “irrelevance filter” elements being abstract elements directed to a mental process and mathematical concepts. As such, these abstract elements are indeed correctly addressed as abstract elements as claimed in the independent claims, in Step 2A Prong 1, as directed by Applicant’s specification.
The limitations are abstract elements that are part of and directed to the recited abstract idea as described above with respect to the first prong of Step 2A, i.e. mental process and mathematical concepts, generally linked to a technical environment, i.e. computer and sensors. Even novel and newly discovered judicial exceptions are still exceptions, despite their novelty. July 2015 Update, p. 3; see SAP America Inc. v. Investpic, LLC, No. 2017-2081, slip op. at 2 (Fed Cir. May 15, 2018).
Simply reciting specific limitations that narrow the abstract idea does not make an abstract idea non-abstract. 79 Fed. Reg. 74631; buySAFE Inc. v. Google, Inc., 765 F.3d 1350, 1355 (2014); see SAP America at p. 12. As discussed in SAP America, no matter how much of an advance the claims recite, when “the advance lies entirely in the realm of abstract ideas, with no plausibly alleged innovation in the non-abstract application realm,” “[a]n advance of that nature is ineligible for patenting.” Id. at p. 3.
As stated in the MPEP, "an improvement in the abstract idea itself ... is not an improvement in technology." MPEP 2106.05(a). Mere automation of a manual process or a business method being applied on a general purpose computer is not sufficient to show an improvement in computers or other technology, and the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology. MPEP 2106.05(a). Further, “the transformation is extra-solution activity or a field-of-use (i.e., the extent to which (or how) the transformation imposes meaningful limits on the execution of the claimed method steps). A transformation that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not provide significantly more (or integrate a judicial exception into a practical application).” MPEP 2106.05(c). Thus, Applicant’s claims do not recite an improvement in technology or integrate into a practical application, but rather mental processes and mathematical concepts implemented using or applying generic computer components.
It is important to note that a mathematical concept need not be expressed in mathematical symbols, because “[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula.” In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a ‘‘series of mathematical calculations based on selected information’’ are directed to abstract ideas); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014) (holding that claims to a ‘‘process of organizing information through mathematical correlations’’ are directed to an abstract idea); and Bancorp Servs., LLC v. Sun Life Assurance Co. of Can. (U.S.), 687 F.3d 1266, 1280, 103 USPQ2d 1425, 1434 (Fed. Cir. 2012) (identifying the concept of ‘‘managing a stable value protected life insurance policy by performing calculations and manipulating the results’’ as an abstract idea).
[M]athematical calculations recited in a claim include:
i. performing a resampled statistical analysis to generate a resampled distribution, SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163-65, 127 USPQ2d 1597, 1598-1600 (Fed. Cir. 2018), modifying SAP America, Inc. v. InvestPic, LLC, 890 F.3d 1016, 126 USPQ2d 1638 (Fed. Cir. 2018);
Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures “can be carried out in existing computers long in use, no new machinery being necessary.” 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of “anonymous loan shopping” recited in a computer system claim is an abstract idea because it could be “performed by humans without a computer”).
Performing a mental process on a generic computer. An example of a case identifying a mental process performed on a generic computer as an abstract idea is Voter Verified, Inc. v. Election Systems & Software, LLC, 887 F.3d 1376, 1385, 126 USPQ2d 1498, 1504 (Fed. Cir. 2018). In this case, the Federal Circuit relied upon the specification in explaining that the claimed steps of voting, verifying the vote, and submitting the vote for tabulation are “human cognitive actions” that humans have performed for hundreds of years. The claims therefore recited an abstract idea, despite the fact that the claimed voting steps were performed on a computer. 887 F.3d at 1385, 126 USPQ2d at 1504. Another example is Versata, in which the patentee claimed a system and method for determining a price of a product offered to a purchasing organization that was implemented using general purpose computer hardware. 793 F.3d at 1312-13, 1331, 115 USPQ2d at 1685, 1699. The Federal Circuit acknowledged that the claims were performed on a generic computer, but still described the claims as “directed to the abstract idea of determining a price, using organizational and product group hierarchies, in the same way that the claims in Alice were directed to the abstract idea of intermediated settlement, and the claims in Bilski were directed to the abstract idea of risk hedging.” 793 F.3d at 1333; 115 USPQ2d at 1700-01.
Performing a mental process in a computer environment. An example of a case identifying a mental process performed in a computer environment as an abstract idea is Symantec Corp., 838 F.3d at 1316-18, 120 USPQ2d at 1360. In this case, the Federal Circuit relied upon the specification when explaining that the claimed electronic post office, which recited limitations describing how the system would receive, screen and distribute email on a computer network, was analogous to how a person decides whether to read or dispose of a particular piece of mail and that “with the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper”. 838 F.3d at 1318, 120 USPQ2d at 1360. Another example is FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 120 USPQ2d 1293 (Fed. Cir. 2016). The patentee in FairWarning claimed a system and method of detecting fraud and/or misuse in a computer environment, in which information regarding accesses of a patient’s personal health information was analyzed according to one of several rules (i.e., related to accesses in excess of a specific volume, accesses during a pre-determined time interval, or accesses by a specific user) to determine if the activity indicates improper access. 839 F.3d. at 1092, 120 USPQ2d at 1294. The court determined that these claims were directed to a mental process of detecting misuse, and that the claimed rules here were “the same questions (though perhaps phrased with different words) that humans in analogous situations detecting fraud have asked for decades, if not centuries.” 839 F.3d. at 1094-95, 120 USPQ2d at 1296.
Using a computer as a tool to perform a mental process. An example of a case in which a computer was used as a tool to perform a mental process is Mortgage Grader, 811 F.3d. at 1324, 117 USPQ2d at 1699. The patentee in Mortgage Grader claimed a computer-implemented system for enabling borrowers to anonymously shop for loan packages offered by a plurality of lenders, comprising a database that stores loan package data from the lenders, and a computer system providing an interface and a grading module. The interface prompts a borrower to enter personal information, which the grading module uses to calculate the borrower’s credit grading, and allows the borrower to identify and compare loan packages in the database using the credit grading. 811 F.3d. at 1318, 117 USPQ2d at 1695. The Federal Circuit determined that these claims were directed to the concept of “anonymous loan shopping”, which was a concept that could be “performed by humans without a computer.” 811 F.3d. at 1324, 117 USPQ2d at 1699. Another example is Berkheimer v. HP, Inc., 881 F.3d 1360, 125 USPQ2d 1649 (Fed. Cir. 2018), in which the patentee claimed methods for parsing and evaluating data using a computer processing system. The Federal Circuit determined that these claims were directed to mental processes of parsing and comparing data, because the steps were recited at a high level of generality and merely used computers as a tool to perform the processes. 881 F.3d at 1366, 125 USPQ2d at 1652-53. See MPEP 2106.04(a)(2).
Further, the courts have indicated may not be sufficient to show an improvement in computer-functionality:
i. Generating restaurant menus with functionally claimed features, Ameranth, 842 F.3d at 1245, 120 USPQ2d at 1857;
ii. Accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016);
iii. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017) or speeding up a loan-application process by enabling borrowers to avoid physically going to or calling each lender and filling out a loan application, LendingTree, LLC v. Zillow, Inc., 656 Fed. App'x 991, 996-97 (Fed. Cir. 2016) (non-precedential);
vii. Providing historical usage information to users while they are inputting data, in order to improve the quality and organization of information added to a database, because “an improvement to the information stored by a database is not equivalent to an improvement in the database’s functionality,” BSG Tech LLC v. Buyseasons, Inc., 899 F.3d 1281, 1287-88, 127 USPQ2d 1688, 1693-94 (Fed. Cir. 2018); and
viii. Arranging transactional information on a graphical user interface in a manner that assists traders in processing information more quickly, Trading Technologies v. IBG LLC, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019).
And, the courts have indicated may not be sufficient to show an improvement to technology include:
i. A commonplace business method being applied on a general purpose computer, Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
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;
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
TLI Communications provides an example of a claim invoking computers and other machinery merely as a tool to perform an existing process. The court stated that the claims describe steps of recording, administration and archiving of digital images, and found them to be directed to the abstract idea of classifying and storing digital images in an organized manner. 823 F.3d at 612, 118 USPQ2d at 1747. The court then turned to the additional elements of performing these functions using a telephone unit and a server and noted that these elements were being used in their ordinary capacity (i.e., the telephone unit is used to make calls and operate as a digital camera including compressing images and transmitting those images, and the server simply receives data, extracts classification information from the received data, and stores the digital images based on the extracted information). 823 F.3d at 612-13, 118 USPQ2d at 1747-48. In other words, the claims invoked the telephone unit and server merely as tools to execute the abstract idea. Thus, the court found that the additional elements did not add significantly more to the abstract idea because they were simply applying the abstract idea on a telephone network without any recitation of details of how to carry out the abstract idea.
Other examples where the courts have found the additional elements to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process include:
i. A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
ii. Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016);
iii. A process for monitoring audit log data that is executed on a general-purpose computer where the increased speed in the process comes solely from the capabilities of the general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016);
iv. A method of using advertising as an exchange or currency being applied or implemented on the Internet, Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715, 112 USPQ2d 1750, 1754 (Fed. Cir. 2014);
v. Requiring the use of software to tailor information and provide it to the user on a generic computer, Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015);
Response to Arguments – Prior Art
Applicant’s arguments with respect to the rejections have been fully considered.
The closest prior art are US Patent Publication to US20080140362A1 to Gross et al., (hereinafter referred to as “Gross”) in view of US Patent Publication to US20160371170A1 to Salunke et al., (hereinafter referred to as “Salunke”)
However, the teachings of the references do not teach the specific ordered sequence of limitations of independent claims 1, 11, 18,
A method for estimating a remaining useful life, RUL, of an electronic device, wherein during a surveillance mode, the method comprises:
receiving a set of time-series signals gathered from sensors in the electronic device while the electronic device is operating;
detecting statistical changes in the set of time-series signals that are deemed as anomalous signal patterns;
generating a set of anomaly alarms, wherein an anomaly alarm is generated for each of the anomalous signal patterns;
applying an irrelevance filter to the set of anomaly alarms to produce filtered anomaly alarms that do not include suspected false alarms, wherein the irrelevance filter operates based on a stored dataset comprising historical time-series signal patterns and associated operational outcomes for a plurality of similar electronic devices, the operational outcomes including operation without incident and failure events, wherein the irrelevance filter removes anomaly alarms associated with one or more anomalous signal patterns that (i) are not correlated with previous failures of the plurality of similar electronic devices based on the stored dataset, and (ii) match signal patterns in the stored dataset that are associated with operation without incident of the plurality of similar electronic devices;
wherein removing the suspected false alarms from the set of anomaly alarms, by the irrelevance filter, comprises removing a target anomaly alarm associated with an anomalous signal pattern when the anomalous signal pattern matches a similar signal pattern from the stored dataset that was previously observed from the similar electrical devices that have operated without incident; and generating a notification indicating an estimated remaining useful life of the electronic device based on at least the anomalous signal patterns associated with the filtered anomaly alarms.
No Non-Patent literature teach the specific ordered sequence of limitations of independent claims 1, 11, 18.
The prior art rejection is hereby withdrawn.
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 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 1 (similarly 11, 18) recite, “A method for estimating a remaining useful life, RUL, of an electronic device, wherein during a surveillance mode, the method comprises:
receiving a set of time-series signals gathered from … while the electronic device is operating;
detecting statistical changes in the set of time-series signals that are deemed as anomalous signal patterns;
generating a set of anomaly alarms, wherein an anomaly alarm is generated for each of the anomalous signal patterns;
applying an irrelevance filter to the set of anomaly alarms to produce filtered anomaly alarms that do not include suspected false alarms, wherein the irrelevance filter operates based on a stored dataset comprising historical time-series signal patterns and associated operational outcomes for a plurality of similar electronic devices, the operational outcomes including operation without incident and failure events, wherein the irrelevance filter removes anomaly alarms associated with one or more anomalous signal patterns that (i) are not correlated with previous failures of the plurality of similar electronic devices based on the stored dataset, and (ii) match signal patterns in the stored dataset that are associated with operation without incident of the plurality of similar electronic devices;
wherein removing the suspected false alarms from the set of anomaly alarms, by the irrelevance filter, comprises removing a target anomaly alarm associated with an anomalous signal pattern when the anomalous signal pattern matches a similar signal pattern from the stored dataset that was previously observed from the similar electrical devices that have operated without incident; and generating a notification indicating an estimated remaining useful life of the electronic device based on at least the anomalous signal patterns associated with the filtered anomaly alarms.”
Analyzing under Step 2A, Prong 1:
The limitations regarding, …estimating a remaining useful life, RUL, of an electronic device… receiving a set of time-series signals gathered from … while the electronic device is operating; detecting statistical changes in the set of time-series signals that are deemed as anomalous signal patterns; generating a set of anomaly alarms, wherein an anomaly alarm is generated for each of the anomalous signal patterns; applying an irrelevance filter to the set of anomaly alarms to produce filtered anomaly alarms that do not include suspected false alarms, wherein the irrelevance filter operates based on a stored dataset comprising historical time-series signal patterns and associated operational outcomes for a plurality of similar electronic devices, the operational outcomes including operation without incident and failure events, wherein the irrelevance filter removes anomaly alarms associated with one or more anomalous signal patterns that (i) are not correlated with previous failures of the plurality of similar electronic devices based on the stored dataset, and (ii) match signal patterns in the stored dataset that are associated with operation without incident of the plurality of similar electronic devices; wherein removing the suspected false alarms from the set of anomaly alarms, by the irrelevance filter, comprises removing a target anomaly alarm associated with an anomalous signal pattern when the anomalous signal pattern matches a similar signal pattern from the stored dataset that was previously observed from the similar electrical devices that have operated without incident; and generating a notification indicating an estimated remaining useful life of the electronic device based on at least the anomalous signal patterns associated with the filtered anomaly alarms.…., under the broadest reasonable interpretation, can include a human using their mind and using pen and paper to perform the above identified limitations; therefore, the claims are directed to a mental process.
Further, … estimating a remaining useful life, RUL, of an electronic device… receiving a set of time-series signals gathered from … while the electronic device is operating; detecting statistical changes in the set of time-series signals that are deemed as anomalous signal patterns; generating a set of anomaly alarms, wherein an anomaly alarm is generated for each of the anomalous signal patterns; applying an irrelevance filter to the set of anomaly alarms to produce filtered anomaly alarms that do not include suspected false alarms, wherein the irrelevance filter operates based on a stored dataset comprising historical time-series signal patterns and associated operational outcomes for a plurality of similar electronic devices, the operational outcomes including operation without incident and failure events, wherein the irrelevance filter removes anomaly alarms associated with one or more anomalous signal patterns that (i) are not correlated with previous failures of the plurality of similar electronic devices based on the stored dataset, and (ii) match signal patterns in the stored dataset that are associated with operation without incident of the plurality of similar electronic devices; wherein removing the suspected false alarms from the set of anomaly alarms, by the irrelevance filter, comprises removing a target anomaly alarm associated with an anomalous signal pattern when the anomalous signal pattern matches a similar signal pattern from the stored dataset that was previously observed from the similar electrical devices that have operated without incident; and generating a notification indicating an estimated remaining useful life of the electronic device based on at least the anomalous signal patterns associated with the filtered anomaly alarms…, are mathematical concepts.
Accordingly, the claims are directed to a mental process, mathematical concepts, and thus, the claims are directed to an abstract idea under the first prong of Step 2A.
Analyzing under Step 2A, Prong 2:
This judicial exception is not integrated into a practical application under the second prong of Step 2A.
In particular, the claims recite the additional elements beyond the recited abstract idea identified under Step 2A, Prong 1, such as:
Claim 1, 11, 18: sensors in the electronic device, A non-transitory computer-readable storage medium storing instructions that when executed by a computing system comprising one or more computing devices, cause the computing system to, system comprising: one or more computing devices comprising at least one processor and at least one associated memory; and a notification mechanism configured to execute on the at least one processor, wherein the notification mechanism is configured to
, and pursuant to the broadest reasonable interpretation, as an ordered combination, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea, and thus, are no more than applying the abstract idea with generic computer components.
Further, these additional elements generally link the abstract idea to a technical environment, namely the environment of a computer.
Additionally, with respect to, “…receiving… gathered from…”, “…based on a stored dataset…”, “…generating a set of anomaly alarms…” “…generating a notification…”, these elements do not add a meaningful limitations to integrate the abstract idea into a practical application because they are extra-solution activity, pre and post solution activity - i.e. data gathering – “…receiving… gathered from…”, “…based on a stored dataset…” data output – “…generating a set of anomaly alarms…”, “…generating a notification…”
Analyzing under Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B.
As noted above, the aforementioned additional elements beyond the recited abstract idea are not sufficient to amount to significantly more than the recited abstract idea because, as an order combination, the additional elements are no more than mere instructions to implement the idea using generic computer components (i.e. apply it).
Additionally, as an order combination, the additional elements append the recited abstract idea to well-understood, routine, and conventional activities in the field as individually evinced by the applicant’s own disclosure, as required by the Berkheimer Memo, in at least:
[0006]The disclosed embodiments provide systems and methods that estimate a remaining useful life (RUL) of an electronic device, which may be a utility system asset, an electro-mechanical device, or other type of electronic-based device. Although the present disclosure is described with reference to a utility system asset as an embodiment, the present systems and methods may be applied to other types of electronic devices. For example, utility system assets may include but are not limited to power transformers, switches, circuit breakers, power storage units (e.g., batteries, cells), power generating systems and/or components (e.g., power generators, solar panels, wind turbines, hydroelectric components, or other type of electronic devices. The present systems and methods may be applied in a similar manner to other electronic devices, for example, including but not limited to, vehicle components including engines, electric vehicle batteries, control systems, etc.; computing systems and computing components including smart devices, phones, laptops, servers, processors, data storage devices, displays/monitors, networking equipment, or other types of computing system-based components.
[0007] In one embodiment, a method performed by the present system estimates a remaining useful life (RUL) of an electronic device is disclosed. For example, during a surveillance mode, the method comprises the following actions/functions. A set of time-series signals gathered from sensors in the electronic device are received while the electronic device is operating. Statistical changes are detected in the set of time-series signals that are deemed as anomalous signal patterns. A set of anomaly alarms are generated, wherein an anomaly alarm is generated for each of the anomalous signal patterns. An irrelevance filter is applied to the set of anomaly alarms to produce filtered anomaly alarms that do not include suspected false alarms, wherein the irrelevance filter removes anomaly alarms associated with one or more anomalous signal patterns that are not correlated with previous failures of similar electronic devices that are similar to the electronic device; wherein removing the suspected false alarms from the set of anomaly alarms, by the irrelevance filter, comprises removing a target anomaly alarm associated with an anomalous signal pattern when the anomalous signal pattern matches a similar signal pattern that was previously observed from the similar electrical devices that have operated without incident. A notification may be generated indicating an estimated remaining useful life and/or that the electronic device has a limited remaining useful life (e.g., device is near a failing point and/or should be replaced).
[0008] In another embodiment, during a surveillance mode, the system iteratively performs the following operations. First, the system receives a set of present time-series signals gathered from sensors in the utility system asset. Next, the system uses an inferential model to generate estimated values for the set of present time-series signals, and performs a pairwise differencing operation between actual values and the estimated values for the set of present time-series signals to produce residuals. The system then performs a sequential probability ratio test (SPRT) on the residuals to produce SPRT alarms. Next, the system applies an irrelevance filter to the SPRT alarms to produce filtered SPRT alarms, wherein the irrelevance filter removes SPRT alarms for signals that are not correlated with previous failures of similar utility system assets. The system then uses a logistic-regression model to compute an RUL-based risk index for the utility system asset based on the filtered SPRT alarms. Finally, when the risk index exceeds a risk-index threshold, the system generates a notification indicating that the electronic device has a limited remaining useful life (e.g., is near a failing point) and should be replaced.
[0022]The following description is presented to enable any person skilled in the art to make and use the present embodiments, and is provided in the context of a particular application and its requirements. Various modifications to the disclosed embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the present embodiments. Thus, the present embodiments are not limited to the embodiments shown, but are to be accorded the widest scope consistent with the principles and features disclosed herein.
[0023]The data structures and code described in this detailed description are typically stored on a computer-readable storage medium, which may be any device or medium that can store code and/or data for use by a computer system. The computer-readable storage medium includes, but is not limited to, volatile memory, non-volatile memory, magnetic and optical storage devices such as disk drives, magnetic tape, CDs (compact discs), DVDs (digital versatile discs or digital video discs), or other media capable of storing computer-readable media now known or later developed.
[0024]The methods and processes described in the detailed description section can be embodied as code and/or data, which can be stored in a computer-readable storage medium as described above. When a computer system reads and executes the code and/or data stored on the computer-readable storage medium, the computer system performs the methods and processes embodied as data structures and code and stored within the computer-readable storage medium. Furthermore, the methods and processes described below can be included in hardware modules. For example, the hardware modules can include, but are not limited to, application-specific integrated circuit (ASIC) chips, field-programmable gate arrays (FPGAs), and other programmable-logic devices now known or later developed. When the hardware modules are activated, the hardware modules perform the methods and processes included within the hardware modules.
[0025]The disclosed embodiments make use of a novel “irrelevance filter,” which mimics the functionality of the human brain’s basal ganglia to facilitate improved RUL prognostics for large populations of high-cost utility grid assets, especially high-voltage transformers. Many industries are presently benefitting from a new science called "biomimicry" that analyzes nature’s best ideas and adapts them for engineering use cases. The invention disclosed herein provides an example of biomimicry.
[0026]Swedish researchers performing MRI studies on human brains discovered that the basal ganglia act as an “irrelevance filter,” which plays a crucial role in human memory and cognition. If the human brain tried to process and store all inputs coming in through the senses, the brain would be overwhelmed. The basal ganglia weeds out unnecessary information, thereby leaving only those details essential to form memories that contribute to survival of a species, such as memories associated with: acquisition of food; avoidance of danger; propagation of the species; and assurance that basic needs are met. It has been shown that humans with the best memories have highly active basal ganglia.
[0030]Hence, what is needed is an “irrelevance filter” that processes time-series signals for utility system assets that have been run to failure, and produces optimal weighting factors for an associated RUL methodology. Note that this is analogous to the functionality of a basal ganglia “filter” for a human brain, which receives large streams of neural “signals” associated with the five primary senses, and periodically “alerts” the human to patterns that have direct relevance to danger, subsistence, or propagation-of-species opportunities.
[0032]Our anomaly discovery process uses a systematic binary hypothesis technique called the “sequential probability ratio test” (SPRT) as an irrelevance filter for large volumes of time-series signals, and identifies small subsets of time-series signals that warrant further pattern-recognition analyses to facilitate anomaly detection. Hence, our new technique substantially reduces RUL-analysis costs by systematically and safely filtering anomaly alerts generated for individual utility system assets so that RUL-analysis operations are only performed for “relevant” signature patterns that are likely to be associated with incipient fault conditions.
[0034]FIG. 1 illustrates an exemplary prognostic-surveillance system 100 in accordance with the disclosed embodiments. As illustrated in FIG. 1, prognostic-surveillance system 100 operates on a set of time-series sensor signals 104 obtained from sensors in an electronic device. In one embodiment as described herein, the electronic device may be a utility system asset 102, such as a power transformer, but other electronic devices may be used. Note that time-series signals 104 can originate from any type of sensor, which can be located in a component in utility system asset 102, including: a voltage sensor; a current sensor; a pressure sensor; a rotational speed sensor; and a vibration sensor.
[0035] During operation of prognostic-surveillance system 100, time-series signals 104 feed into a time-series database 106, which stores the time-series signals 104 for subsequent analysis. Next, the time-series signals 104 either feed directly from utility system asset 102 or from time-series database 106 into a non-linear, non-parametric (NLNP) regression model 108. Upon receiving the time-series sensor signals 104, NLNP regression model 108 performs a non-linear, non-parametric regression analysis on the samples (including a “current sample”). When the analysis is complete, NLNP regression model 108 outputs estimated signal values 110.
[0036] In one embodiment of the present invention, NLNP regression model 108 uses a multivariate state estimation technique (“MSET”) to perform the regression analysis. Note that the term MSET as used in this specification refers to a technique that loosely represents a class of pattern recognition techniques. (For example, see [Gribok] “Use of Kernel Based Techniques for Sensor Validation in Nuclear Power Plants,” by Andrei V. Gribok, J. Wesley Hines, and Robert E. Uhrig, The Third American Nuclear Society International Topical Meeting on Nuclear Plant Instrumentation and Control and Human-Machine Interface Technologies, Washington DC, November 13-17, 2000.) Hence, the term “MSET” as used in this specification can refer to any technique outlined in [Gribok], including Ordinary Least Squares (OLS), Support Vector Machines (SVM), Artificial Neural Networks (ANNs), MSET, or Regularized MSET (RMSET). Although it is advantageous to use MSET for pattern-recognition purposes, the disclosed embodiments can generally use any one of a generic class of pattern-recognition techniques called nonlinear, nonparametric (NLNP) regression, which includes neural networks, support vector machines (SVMs), auto-associative kernel regression (AAKR), and even simple linear regression (LR).
[0037] Before MSET is used to monitor a system, a model is constructed from which estimates of the system's correct operational state are made. The model is derived empirically from observations made during a training phase on the real system under expected normal operating conditions. Relationships among the signals are learned during the training phase, and these relationships then are used in the surveillance phase of the algorithm to compute estimates of the system state.
[0053] Returning back to FIG. 1, NLNP regression model 108 is “trained” to learn patterns of correlation among the time-series signals 104. This training process involves a one-time, computationally intensive computation, which is performed offline with accumulated data that contains no anomalies. The pattern-recognition system is then placed into a “real-time surveillance mode,” wherein the trained NLNP regression model 108 predicts what each signal should be, based on other correlated variables; these are the “estimated signal values” 110 illustrated in FIG.1. Next, the system uses a difference module 112 to perform a pairwise differencing operation between the actual signal values and the estimated signal values to produce residuals 114, which are passed into SPRT module 116. For the embodiment of the present invention that uses MSET regression analysis, the residual can be calculated using the following expression:
[0054] R(t) = X(t) - MSET(X(t)).
[0055] SPRT module 116 then performs a “detection operation” on the residuals 114 to detect anomalies and possibly to generate SPRT alarms 118. SPRT module uses the sequential probability ratio test (SPRT) proposed by Wald to detect subtle statistical changes in a stationary noisy sequence of observations at the earliest possible time. (See Wald, Abraham, June 1945, “Sequential Tests of Statistical Hypotheses,” Annals of Mathematical Statistics,16(2): 117–186.) For purposes of exposing the details of the SPRT, assume that the monitored process signal Y is normally distributed with mean zero and standard deviation σ (processes with nonzero mean μ can be transformed into a zero-meaned process by subtracting μ from each observation). Process signal Y is said to be degraded if the observations made on Y appear to be distributed about mean M with normal (Gaussian) distribution instead of mean zero, where M is a predetermined system disturbance magnitude. [0073] Referring to FIG. 1, NLNP regression model 108 and difference module 112 work together to remove (filter) the dynamics in the signals X(t) so that the residual R(t) is a stationary random process when the system is in good condition. As the system ages or degrades due to a failure mechanism, the statistical properties of the residual change. This change is detected by SPRT module 116, which generates corresponding SPRT alarms 118.
[0082] Next, the system applies an irrelevance filter to the anomaly alarms (e.g., SPRT alarms) to produce a filtered anomaly alarms (e.g., SPRT alarms), wherein the irrelevance filter removes SPRT alarms for signals that are not correlated with previous failures of similar utility system assets (step 210).
[0083] The system then uses a logistic-regression model to compute an RUL-based risk index for the utility system asset based on tripping frequencies of the filtered SPRT alarms (step 212). If the risk index exceeds a risk-index threshold, the system generates a notification indicating that the electronic device has a limited remaining useful life (e.g., is near a predicted failing point) and should be replaced (step 214).
[0085] FIG. 4 presents a flow chart illustrating a process for training a logistic-regression model to predict an RUL for an asset and for configuring an associated irrelevance filter
[0086] The irrelevance filter is also configured to remove SPRT alarms (e.g., anomaly alarms) that are not relevant (step 416). SPRT alarms that are not relevant include alarms that occur in time intervals that are not near a failure time of the asset/device (e.g., a time beyond/outside the time threshold).
[0087]Various modifications to the disclosed embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the present invention. Thus, the present invention is not limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.
[0088]The foregoing descriptions of embodiments have been presented for purposes of illustration and description only. They are not intended to be exhaustive or to limit the present description to the forms disclosed. Accordingly, many modifications and variations will be apparent to practitioners skilled in the art. Additionally, the above disclosure is not intended to limit the present description. The scope of the present description is defined by the appended claims.
Furthermore, as an ordered combination, these elements amount to generic computer components receiving or transmitting data over a network, performing repetitive calculations, electronic record keeping, and storing and retrieving information in memory, which, as held by the courts, are well-understood, routine, and conventional. See MPEP 2106.05(d).
Moreover, the remaining elements of dependent claims do not transform the recited abstract idea into a patent eligible invention because these remaining elements merely recite further abstract limitations that provide nothing more than simply a narrowing of the abstract idea recited in the independent claims.
Looking at these limitations as an ordered combination adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use a generic arrangement of generic computer components to “apply” the recited abstract idea, perform insignificant extra-solution activity, and generally link the abstract idea to a technical environment. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself. Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action.
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/PO HAN LEE/Primary Examiner, Art Unit 3623
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