DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This office action is a response to an amendment/arguments filed on 2/27/2026 which was in response to the office action mailed on 11/5/2025 (hereinafter the prior office action).
Claim(s) 1-13 and 18-28 is/are pending.
Claim(s) 1, 8-9, 18 and 23-24 is/are amended.
Claim(s) 14-17 is/are cancelled.
Claim(s) 25-28 is/are new.
Response to Arguments
Applicant’s arguments, filed on 2/27/2026, have been fully considered but they are not persuasive.
Applicant states in Pg. 9 in “Remarks” that the examiner agreed that the claim amendments/arguments discussed during the interview and presented herein likely overcome the current rejections.
Examiner respectfully notes that for the interview conducted on 2/24/2026, an interview summary was sent out on 2/26/2026, which included the examiner’s statement that more search and consideration would be needed to determine if the proposed amendments overcome the prior art of record. Examiner respectfully states that no indication was made that the amendments likely overcome the current rejections because further consideration and/or search had taken place yet.
Applicant further states in Pg. 10-11 in “Remarks” that Muddu itself does not describe Applicant’s detecting anomalies based on modified sensor data that includes estimated sensor data replacing data that cannot be validated. Applicant further states that Wang itself also does not describe the above limitation.
Examiner respectfully disagrees because, in response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). In this case, it is Muddu that teaches detecting anomalies based on sensor data (Muddu, Para. 137), while Wang teaches modifying sensor data by replacing data that cannot be validated with estimated sensor data (Wang, Para. 43, 46). Thus, it is neither Muddu, nor Wang, taken by itself, but the combination of Muddu and Wang, that teaches the claim limitation.
Applicant further states in Pg. 11 in “Remarks” that one of ordinary skill in the art would not have modified the cited references to provide these features.
Examiner respectfully disagrees because, in the absence of further arguments describing why one of ordinary skill in the art would not have modified the cited references, examiner notes that, in response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, one of ordinary skill in the art would have been motivated to do this modification in order to replace signal from a failed sensor, as suggested by Wang (Para. 46).
Claim Warnings
Applicant is advised that should claim 26 be found allowable, claim 27 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), fourth paragraph:
Subject to the [fifth paragraph of 35 U.S.C. 112 (pre-AIA )], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim(s) 7 and 25 is/are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Claims 7 recites identifying the anomalous event based on the current sensor data with the estimated sensor data substituted for the particular subset of the current sensor data. This is the same as is recited in newly amended claim 1 (on which claim 7 depends). Claim 1 recites detecting, i.e. identifying, an anomalous event based on modified current sensor data. Claim 1 further recites that the modified sensor data is the estimated sensor data being substituted for the particular subset of the current sensor data, which is what claim 7 recites. Claim 25 is rejected under the same rationale as for claim 7 outlined above.
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claim(s) 1-13 and 18-28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Muddu et al. (U.S. Pub. No. 2020/0021607) (hereinafter “Muddu”) in view of Wang et al. (U.S. Pub. No. 2020/0184351) (hereinafter “Wang”).
Regarding claim 1, Muddu teaches a non-transitory computer readable medium comprising instructions which, when executed by one or more hardware processors, cause performance of operations (Para. 135 - - system is implemented in a computing system)
comprising: training a first machine learning model based on historical sensor data obtained from a plurality of data sources; (Para. 187 - - machine learning model is used; Para. 189 - - sensor data is used from data sources like industrial systems; Para. 150 - - data includes stored historical data related to past events)
applying the first trained machine learning model to current sensor data detected by a plurality of data sources to identify a particular subset of the current sensor data that cannot be validated based on data relationships corresponding to the historical sensor data; (Para. 151 - - anomalies in data are uncovered, where anomalous data is data that cannot be validated; Para. 150 - - machine learning model uses stored historical data)
…detecting an anomalous event based on the modified current sensor data with the estimated sensor data substituted for the particular subset of the current sensor data; (Para. 137 - - remediation of detected anomaly is enabled, which requires identifying anomalous event based on the data)
…and generating a task list to remediate the anomalous event. (Para. 137 - - remediation of detected anomaly is enabled, i.e. task list is generated to remediate anomalous event)
But Muddu does not explicitly teach applying a second trained machine learning model to generate estimated sensor data to substitute for the particular subset of the current sensor data that cannot be validated;
generating modified current sensor data by substituting the estimated sensor data for the particular subset of the current sensor data;
However, Wang teaches applying a second trained machine learning model to generate estimated sensor data to substitute for the particular subset of the current sensor data that cannot be validated; (Para. 43, 46 - - estimated signals are substituted for actual signals)
generating modified current sensor data by substituting the estimated sensor data for the particular subset of the current sensor data; (Para. 43, 46 - - estimated signals are substituted for actual signals)
Muddu and Wang are analogous art because they are from the same field of endeavor and contain overlapping structural and/or functional similarities. They both contain detecting anomalies and taking action.
Therefore, before the effective filing date of the claimed invention (AIA ), it would have been obvious to a person of ordinary skill in the art to modify the above limitation(s) as taught by Muddu, by incorporating the above limitation(s) as taught by Wang.
One of ordinary skill in the art would have been motivated to do this modification in order to replace signal from a failed sensor, as suggested by Wang (Para. 46).
Regarding claim 2, Muddu further teaches wherein the task list specifies, for a particular task, (a) an entity to perform the particular task, (b) an action to be performed, and (c) a component of a monitored system on which the action is to be performed. (Para. 151 - - action is triggered in response to anomalies, where the action is performed by the system, i.e. entity to perform the task, and where the action to be performed is specified, and where the action is directed to a monitored system component)
Regarding claim 3, Muddu further teaches wherein the task list comprises an ordered sequence of two or more tasks. (Para. 142 - - actions are automated in response, where automatic response includes ordered sequence of multiple tasks)
Regarding claim 4, Muddu further teaches wherein at least one first task among the two or more tasks is a task to be performed by a first entity, wherein at least one second task among the two or more tasks is a task to be performed by a second entity, different from the first entity. (Para. 211 - - two entities are involved in performing actions)
Regarding claim 5, Muddu further teaches wherein the two or more tasks further specify: a dependency of one task, among the two or more tasks, on another task, (Para. 142 - - actions are automated in response, where automatic response includes one task being performed based on the previous task, i.e. dependency)
among the two or more tasks, wherein at least one task among the two or more tasks includes a dependency upon another task among the two or more tasks, (Para. 142 - - actions are automated in response, where automatic response includes one task being performed based on the previous task, i.e. dependency)
and wherein the two or more tasks are arranged in a sequence according to the dependency. (Para. 142 - - actions are automated in response, where automatic response includes ordered sequence of multiple tasks, and where one automatic task depends on the previous automatic task)
Regarding claim 6, Muddu further teaches wherein at least one first task among the two or more tasks is a task to be performed by a human, (Para. 151 - - human operator performs a task within a sequence of tasks)
wherein at least one second task among the two or more tasks is a task to be performed by a computer without human intervention, (Para. 151 - - at least one task is automated)
and wherein the instructions further cause performance of operations comprising: responsive to detecting completion of the at least one second task: generating a human-readable notification associated with the completion of the at least one second task. (Para. 151 - - automated task completes and proceeds to notify human operator regarding second human task)
Regarding claim 7, Muddu further teaches identifying the anomalous event based on the current sensor data with the estimated sensor data substituted for the particular subset of the current sensor data. (Para. 137 - - remediation of detected anomaly is enabled, which requires identifying anomalous event based on the data)
Regarding claim 8, Muddu further teaches wherein the first trained machine learning model and the second trained machine learning model correspond to a same machine learning model. (Para. 177 - - one analysis module uses machine learning model used by another analysis module, i.e. the same model is shared)
Regarding claim 9, Wang further teaches wherein the same machine learning model is a multivariate state estimation technique (MSET) model. (Para. 42 - - MSET model is used)
One of ordinary skill in the art would have been motivated to do this modification in order to replace signal from a failed sensor, as suggested by Wang (Para. 46).
Regarding claim 10, Muddu further teaches wherein the operations further comprise: obtaining a training data set from the historical sensor data; (Para. 150 - - historical data is used to improve, i.e. train)
training a second machine learning model to identify correlations among the plurality of data sources based on the training data set; (Para. 177 - - one analysis module uses machine learning model used by another analysis module, i.e. same training is applied to second module)
wherein applying the second trained machine learning model to generate estimated sensor data to substitute for the particular subset of the current sensor data that cannot be validated comprises: identifying, by the second trained machine learning model the correlations among the particular subset of the current sensor data and another subset of the current sensor data that is validated; (Para. 407 - - data sets are correlated, i.e. identify correlations within data)
Wang futher teaches and generating the estimated sensor data based on the correlations. (Para. 43, 46 - - estimated signals are substituted for actual signals)
One of ordinary skill in the art would have been motivated to do this modification in order to replace signal from a failed sensor, as suggested by Wang (Para. 46).
Regarding claim 11, Muddu further teaches wherein the task list to remediate the anomalous event comprises tasks to remediate a root cause of the anomalous event. (Para. 151 - - various examples of remediating the root cause of an anomaly)
Regarding claim 12, Muddu further teaches responsive to receiving an input to modify two or more tasks associated with the root cause, modifying the two or more tasks by performing one or both of: adding a new task to the two or more tasks; and removing at least one task from among the two or more tasks; (Para. 142 - - actions are automated in response, where automatic response includes adding/removing multiple tasks)
and re-generating the task list based on the modifying the two or more tasks. (Para. 142 - - actions are automated in response, where actions are re-generated)
Regarding claim 13, Muddu further teaches wherein generating the task list comprises: generating at least one query based on the root cause; (Para. 151 - - various examples of remediating the root cause of an anomaly)
and querying a set of stored task templates to identify two or more tasks satisfying query conditions associated with the at least one query. (Para. 142 - - actions are automated in response, where automatic actions include known tasks, i.e. task templates, that satisfy the root cause query)
Regarding claim 18, Muddu teaches a method, comprising: training a first machine learning model based on historical sensor data obtained from a plurality of data sources; (Para. 187 - - machine learning model is used; Para. 189 - - sensor data is used from data sources like industrial systems; Para. 150 - - data includes stored historical data related to past events)
applying the first trained machine learning model to current sensor data detected by a plurality of data sources to identify a particular subset of the current sensor data that cannot be validated based on data relationships corresponding to the historical sensor data; (Para. 151 - - anomalies in data are uncovered, where anomalous data is data that cannot be validated; Para. 150 - - machine learning model uses stored historical data)
…detecting an anomalous event based on the modified current sensor data with the estimated sensor data substituted for the particular subset of the current sensor data; (Para. 137 - - remediation of detected anomaly is enabled, which requires identifying anomalous event based on the data)
…and generating a task list to remediate the anomalous event. (Para. 137 - - remediation of detected anomaly is enabled, i.e. task list is generated to remediate anomalous event)
But Muddu does not explicitly teach applying a second trained machine learning model to generate estimated sensor data to substitute for the particular subset of the current sensor data that cannot be validated;
generating modified current sensor data by substituting the estimated sensor data for the particular subset of the current sensor data;
However, Wang teaches applying a second trained machine learning model to generate estimated sensor data to substitute for the particular subset of the current sensor data that cannot be validated; (Para. 43, 46 - - estimated signals are substituted for actual signals)
generating modified current sensor data by substituting the estimated sensor data for the particular subset of the current sensor data; (Para. 43, 46 - - estimated signals are substituted for actual signals)
Muddu and Wang are analogous art because they are from the same field of endeavor and contain overlapping structural and/or functional similarities. They both contain detecting anomalies and taking action.
Therefore, before the effective filing date of the claimed invention (AIA ), it would have been obvious to a person of ordinary skill in the art to modify the above limitation(s) as taught by Muddu, by incorporating the above limitation(s) as taught by Wang.
One of ordinary skill in the art would have been motivated to do this modification in order to replace signal from a failed sensor, as suggested by Wang (Para. 46).
Regarding claim 19, Muddu further teaches wherein the task list specifies, for a particular task, (a) an entity to perform the particular task, (b) an action to be performed, and (c) a component of a monitored system on which the action is to be performed. (Para. 151 - - action is triggered in response to anomalies, where the action is performed by the system, i.e. entity to perform the task, and where the action to be performed is specified, and where the action is directed to a monitored system component)
Regarding claim 20, Muddu further teaches wherein the task list comprises an ordered sequence of two or more tasks. (Para. 142 - - actions are automated in response, where automatic response includes ordered sequence of multiple tasks)
Regarding claim 21, Muddu further teaches wherein at least one first task among the two or more tasks is a task to be performed by a first entity, and wherein at least one second task among the two or more tasks is a task to be performed by a second entity, different from the first entity. (Para. 211 - - two entities are involved in performing actions)
Regarding claim 22, Muddu further teaches wherein the two or more tasks further specify: a dependency of one task, among the two or more tasks, on another task, among the two or more tasks, (Para. 142 - - actions are automated in response, where automatic response includes one task being performed based on the previous task, i.e. dependency)
wherein at least one task among the two or more tasks includes a dependency upon another task among the two or more tasks, (Para. 142 - - actions are automated in response, where automatic response includes one task being performed based on the previous task, i.e. dependency)
and wherein the two or more tasks are arranged in the ordered sequence according to the dependency. (Para. 142 - - actions are automated in response, where automatic response includes ordered sequence of multiple tasks, and where one automatic task depends on the previous automatic task)
Regarding claim 23, Muddu further teaches wherein the first machine learning model and the second machine learning model correspond to a same machine learning model, (Para. 177 - - one analysis module uses machine learning model used by another analysis module, i.e. the same model is shared)
Wang further teaches and wherein the same machine learning model is a multivariate state estimation technique (MSET) model. (Para. 42 - - MSET model is used)
Regarding claim 24, Muddu teaches a system comprising: one or more processors; and memory storing instructions that, when executed by the one or more processors, cause the system to perform: (Para. 135 - - system is implemented in a computing system)
training a first machine learning model based on historical sensor data obtained from a plurality of data sources; (Para. 187 - - machine learning model is used; Para. 189 - - sensor data is used from data sources like industrial systems; Para. 150 - - data includes stored historical data related to past events)
applying the first trained machine learning model to current sensor data detected by a plurality of data sources to identify a particular subset of the current sensor data that cannot be validated based on data relationships corresponding to the historical sensor data; (Para. 151 - - anomalies in data are uncovered, where anomalous data is data that cannot be validated; Para. 150 - - machine learning model uses stored historical data)
…detecting an anomalous event based on the modified current sensor data with the estimated sensor data substituted for the particular subset of the current sensor data; (Para. 137 - - remediation of detected anomaly is enabled, which requires identifying anomalous event based on the data)
…and generating a task list to remediate the anomalous event. (Para. 137 - - remediation of detected anomaly is enabled, i.e. task list is generated to remediate anomalous event)
But Muddu does not explicitly teach applying a second trained machine learning model to generate estimated sensor data to substitute for the particular subset of the current sensor data that cannot be validated;
generating modified current sensor data by substituting the estimated sensor data for the particular subset of the current sensor data;
However, Wang teaches applying a second trained machine learning model to generate estimated sensor data to substitute for the particular subset of the current sensor data that cannot be validated; (Para. 43, 46 - - estimated signals are substituted for actual signals)
generating modified current sensor data by substituting the estimated sensor data for the particular subset of the current sensor data; (Para. 43, 46 - - estimated signals are substituted for actual signals)
Muddu and Wang are analogous art because they are from the same field of endeavor and contain overlapping structural and/or functional similarities. They both contain detecting anomalies and taking action.
Therefore, before the effective filing date of the claimed invention (AIA ), it would have been obvious to a person of ordinary skill in the art to modify the above limitation(s) as taught by Muddu, by incorporating the above limitation(s) as taught by Wang.
One of ordinary skill in the art would have been motivated to do this modification in order to replace signal from a failed sensor, as suggested by Wang (Para. 46).
Regarding claim 25, Muddu further teaches wherein detecting the anomalous event is based at least in part on the estimated sensor data substituted for the particular subset of the current sensor data. (Para. 137 - - remediation of detected anomaly is enabled, which requires identifying anomalous event based on the data)
Regarding claim 26, Muddu further teaches wherein detecting the anomalous event is based on (a) the estimated sensor data substituted for the particular subset of the current sensor data (Para. 137 - - remediation of detected anomaly is enabled, which requires identifying anomalous event based on the data)
and (b) an additional subset of the current sensor data that was validated based on applying the first machine learning model to the current sensor data. (Para. 455 - - anomalies, i.e. data that cannot be validated, are filtered out, i.e. validated data is generated)
Regarding claim 27, Muddu further teaches wherein the modified current sensor data includes the estimated sensor data and an additional subset of the current sensor data that was validated based on applying the first machine learning model to the current sensor data. (Para. 137 - - remediation of detected anomaly is enabled, which requires identifying anomalous event based on the data; Para. 455 - - anomalies, i.e. data that cannot be validated, are filtered out, i.e. validated data is generated)
Regarding claim 28, Muddu further teaches determining a set of parameters required to perform a first task in the task list; (Para. 142 - - actions are automated in response, where automatic response includes identifying the necessary parameters to generate tasks)
determining a set of required sensor values corresponding to the set of parameters; (Para. 705 - - only features needed for determining are identified, i.e. required sensor values are used)
…and generating the first task using the estimated sensor data. (Para. 137 - - remediation of detected anomaly is enabled, i.e. task list is generated to remediate anomalous event)
Wang further teaches and based on determining the set of required sensor values includes one or more values from the particular subset of the current sensor data that cannot be validated: replacing the one or more values with one or more estimated values from the estimated sensor data; (Para. 43, 46 - - estimated signals are substituted for actual signals)
It is noted that any citations to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the reference 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. See MPEP 2123.
Citation of Pertinent Prior Art
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Pub. No. 2023/0061280 by Rohrkemper et al., which discloses root cause analysis for deterministic machine learning model (Title/Abstract).
U.S. Pub. No. 2021/0203157 by Visweswariah et al., which discloses scalable assessing of healthy condition scores in renewable asset management (Title/Abstract).
U.S. Pat. No. 11,637,740 by Sharma et al., which discloses intelligent anomaly detection and root cause analysis in mobile networks (Title/Abstract).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Saad M. Kabir whose telephone number is 571-270-0608 (direct fax number is 571-270-9933). The examiner can normally be reached on Mondays to Fridays 9am to 5pm EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mohammad Ali can be reached on 571-272-4105. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SAAD M KABIR/
Examiner, Art Unit 2119
/ZIAUL KARIM/Primary Examiner, Art Unit 2119
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