DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 05/15/2026 has been entered.
The status of the claims is as follows.
Claims 1, 11 and 16 are amended. Claims 1-20 are currently pending.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-6, 9-10; 11-12, 15; 16-17, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Bukhsh et al. (“Predictive Maintenance for Infrastructure Asset Management” [2020], hereinafter “Bukhsh”) in view of Serradilla et al. (“Deep learning models for predictive maintenance: a survey, comparison, challenges and prospect” [2020], hereinafter “Serradilla”) in view of Chu et al. (US20180096261A1, hereinafter “Chu”).
Regarding Claim 1,
Bukhsh discloses executing an automated process to be used to maintain physical assets of a selected environment (Bukhsh [Figure 3];
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Bukhsh [Page 41 Column 2 Paragraph 3]; “Instead of regular inspections, PdM proposes to perform continuous monitoring and reporting of remote assets by utilizing edge computing and sensing systems.” Wherein deployment of the model for continuous monitoring, analysis and reporting thus reads on such a process for maintaining physical assets to be an automated process)
the automated process including: automatically selecting, as part of executing the automated process, a maintenance solution pipeline from a plurality of maintenance solution pipelines based on obtained information from an artificial intelligence process, the artificial intelligence process being an automated, trained machine learning process and the obtained information including a risk estimation (Bukhsh [Figure 3];
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Wherein the optimization of maintenance plans during the automated process comprising the collection of sensor information and machine learning analysis of collected information thus reads on the automated process comprising selection of some maintenance solution pipeline from a plurality of maintenance solution pipelines, wherein the artificial intelligence process is an automated trained machine learning process; where the obtained information comprising future performance level as well as alerts associated with possible failure details of the future thus reads on the obtained information from the machine learning process comprising some risk estimation)
the maintenance solution pipeline to be used in providing a physical asset maintenance solution for a plurality of physical assets … wherein the plurality of physical assets includes at least one physical component for which physical asset condition-based maintenance is performed (Bukhsh [Page 42 Section “Asset Monitoring”]; “Condition assessment of structures relies heavily on visual inspections due to its affordability and nondestructiveness. Since visual inspections are susceptible to subjective judgments of inspectors, the different types of sensors are instrumented on the structure to record various parameters. The example of a few sensors and recorded parameter include temperature sensors, strain gauges, inclinometers to measure angle and slope, string pots to gauge linear position and 42 velocity, and accelerometers. Depending on the geometry of assets, other monitoring instruments such as unmanned aerial vehicles, laser scanning, and measurement machines can be used.” Wherein the optimized maintenance solution pipelines are used in the context of maintenance of physical assets)
initiating code and model rendering for the maintenance solution pipeline automatically selected to produce the physical asset maintenance solution for the plurality of physical assets (Bukhsh [Page 43 Section “Predictive Modeling”]; “In this step, the augmented data and preliminary analysis are used to develop data-driven prognostic and diagnostic models using machine learning algorithms. The possible use cases of predictive modeling for infrastructure asset management include: i. Will the structure fail within the specified time interval? (Binary classification); ii. Which maintenance actions are needed given the damage descriptions? (Multiclass classification); iii. What is the remaining service life of an asset? (Regression); iv. What is the leading cause of failure? (Interpretability). The machine learning models can be developed to answer these specific questions using the historical data from sensor streams, asset register, maintenance records, and operational details. Given the type of problem as classification, regression or anomaly detection, different machine learning algorithms such as tree-based algorithms, linear regression, support vector machines, and neural networks can be employed to develop accurate predictive models. The developed models can then be bootstrapped into web APIs to integrate within core infrastructure for sending alerts to asset managers in case of possible detected damage or near-failure state of assets” wherein initialization of the development of data-driven prognostic and diagnostic models according to the maintenance solution pipeline deemed optimal by the asset analysis thus reads on initiating some code and model rendering for the optimal maintenance solution pipeline selected to produce the physical asset maintenance solution for the plurality of physical assets)
continuing to obtain output from the artificial intelligence process, the output including an automatically generated risk estimation relating to one or more conditions of at least one physical asset of the plurality of physical assets (Bukhsh [Page 42 Section “Performance Assessment”]; “Several monitoring instruments can record diverse parameters from an asset/structure. However, acquisition of the quality data and con verting it into useful information is a substantial challenge. Data collected from diverse procedures require different types of analysis, such as signal processing, time-series analysis for sensory data, and image processing from images and video data. Several preprocessing steps, including noise reduction, elimination of irrelevant features, imputing missing input have to be performed for robust performance assessment of an asset. Additionally, the ground-truth values (data labels) must also be established to comprehend the processed data and use it for predictive modeling. In addition to knowledge of domain experts, different models such as survival analysis, similarity models, mean-time to failure can be used for data labeling.” wherein the artificial intelligence process output comprising signal processing, time-series analysis, or image processing outputting processed data to be used in predictive modeling in comparison against established ground-truth values thus reads on some automatically generated risk estimation (since measuring ground-truth value of parameters such as temperature, strain, inclinometers, string pots against processed data values thus reads on some generated risk estimation of the conditions of physical assets of the plurality of physical assets))
Bukhsh fails to explicitly disclose but Serradilla discloses re-initiating code and model rendering for the maintenance solution pipeline, based on the output from the artificial intelligence process, … regenerating another maintenance solution pipeline (Serradilla [Page 5 Section 2.2.3]; “The anomaly detection methods need preprocessed and some also depend on feature engineered data to work. Once worked on features, the next step is to select, train and optimise the right model for the use-case. Following PdM stages will be influenced and constrained by the selected AD method and use-case’s data” wherein the optimization of model selection, training, and optimization comprising re-initiating of the model and its code rendering reads on regeneration of the same maintenance solution pipeline that was selected to thus produce another, optimized version of the regenerated maintenance solution pipeline)
It would have been obvious to perform Serradilla’s re-initiating of model rendering directed towards model optimization upon Bukhsh’s maintenance solution data-driven prognostic models developed for physical asset maintenance. One would have been motivated to do in order to “optimise the model’s key parameters to adapt it to signal features” (Serradilla [Section 3.7 Paragraph 3]).
Bukhsh/Serradilla fails to disclose but Chu discloses wherein the maintenance solution pipeline automatically selected is reused … reducing processor execution time and memory utilization (Chu [0050]; “As noted above, an anomaly detection logic 220 may be provided to access an anomaly detection model generated by an anomaly management system 215 and detect anomalies in data delivered to the management system from devices (e.g., 105b,d) within an M2M system. The anomaly detection logic 220 may additionally log the reported anomalies and may determine maintenance or reporting events based on the receipt of one or more anomalies”
Chu [0052]; “In some implementations, a user may select a collection of different diversified unsupervised machine learning algorithms for use in generating an anomaly detection model for a particular set of sensors. In some cases, the ensemble manager 245 may self-identify one or more of the unsupervised machine learning algorithms 270, for instance, by identifying one or more of the sensors in the set for which the ensemble is to be created. For instance, the ensemble manager 245 may identify that a particular one of the sensors is of a particular type or model and identify, for instance, from a library or other collection of available machine learning algorithms 270, which of the algorithms would be relevant for detecting anomalies in data generated by the particular sensor. The ensemble manager 240, in some cases, may reuse the overlapping sets of unsupervised machine learning algorithms in the development of different ensembles” wherein the ensemble comprises reused optimized selected anomaly detection models; wherein the maintenance determination pipeline comprising the re-initiated and model rendered reused anomaly detection ensemble being used in a management maintenance system thus reads on a maintenance solution pipeline automatically selected (optimized for anomaly detection) being reused; wherein recycling already optimized selected anomaly detection models thus implicitly reads on reducing processor execution time (since no future optimization necessary) and memory utilization (no creation of additional unsupervised machine learning models during ensemble development))
It would have been obvious to modify Bukhsh/Serradilla’s maintenance solution pipeline selection through artificial intelligence processes to perform Chu’s method of reusing parts of previous pipeline iterations to re-initiate code for a reused maintenance solution pipeline. One would have been motivated to do so “for use in generating anomaly detection models for different sensors or groups of sensors” (Chu [0052]) thus allowing future pipeline iterations to learn for different sensor types from prior iteration trainings.
Bukhsh/Serradilla discloses to produce the physical asset maintenance solution for the plurality of physical assets … regenerating another maintenance solution pipeline. Chu discloses wherein the maintenance solution pipeline automatically selected is reused … reducing processor execution time and memory utilization. By using Chu’s alternative method of reusing maintenance solution pipelines over Bukhsh/Serradilla’s original physical asset maintenance solution pipeline regeneration methodology, the combination of Bukhsh/Serradilla/Chu thus discloses wherein the maintenance solution pipeline automatically selected to produce the physical asset maintenance solution for the plurality of physical assets is reused instead of regenerating another maintenance solution pipeline, reducing processor execution time and memory utilization.
Regarding Claim 2,
The combination of Bukhsh/Serradilla/Chu teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). The combination further discloses wherein the physical asset maintenance solution includes a condition-based maintenance plan for the plurality of physical assets, at least a portion of the plurality of physical assets being interdependent (Bukhsh [Page 42 Section “Asset Monitoring]; “Condition assessment of structures relies heavily on visual inspections due to its affordability and nondestructiveness. Since visual inspections are susceptible to subjective judgments of inspectors, the different types of sensors are instrumented on the structure to record various parameters. The example of a few sensors and recorded parameter include temperature sensors, strain gauges, inclinometers to measure angle and slope, string pots to gauge linear position and velocity, and accelerometers. Depending on the geometry of assets, other monitoring instruments such as unmanned aerial vehicles, laser scanning, and measurement machines can be used” wherein the condition-based maintenance of structure comprising a plurality of assets thus reads on a plurality of interdependent physical assets and an associated condition-based maintenance plan)
Regarding Claim 3,
The combination of Bukhsh/Serradilla/Chu teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). The combination further discloses wherein the physical asset maintenance solution includes a condition-based maintenance schedule for the plurality of physical assets, at least a portion of the plurality of physical assets being interdependent (Bukhsh [Page 41 Section “Modeling Framework of PDM Solution”]; “Typically, the maintenance of infrastructure follows three types of policies, i.e., run-to-failure, planned maintenance, and condition-based maintenance (CBM). Since the scheduled maintenance leads to the more, the better syndrome of undue maintenance and the run-to-failure approach results in higher replacement costs; CBM has been one of the most preferred policies for the past few decades. Figure 2 provides an overview of typical maintenance policies along with PdM in the context of event criticality and time to maintain. The advancements of information and communication technologies have re-branded the CBM policy with the name of PdM. In its essence, PdM is similar to CBM, where the performance state of an asset drives the maintenance deci sions. However, there are two critical differences between the PdM andCBM. 1. Instead of regular inspections, PdM proposes to perform continuous monitoring and reporting of remote assets by utilizing edge computing and sensing systems. 2. In contrast to developing structural-specific physical models, the (sensor) data is ana lyzed by machine learning techniques that notify about the current state and predict future conditions and maintenance needs. By access to monitoring data using several sensors, digital technologies, and artificial intelligence techniques, the ambition of PdM is to develop self-diagnostic systems that alert the assets managers just-in-time in need of an intervention.” Wherein the maintenance solutions comprising active monitoring of assets through predictive maintenance in order to schedule asset manager interventions when appropriate thus reads on some condition-based maintenance schedule)
Regarding Claim 4,
The combination of Bukhsh/Serradilla/Chu teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). The combination fails to explicitly disclose but Serradilla further discloses wherein risk estimation includes an one or more risk estimation scores relating to one or more conditions of one or more physical assets of the plurality of physical assets (Serradilla [Page 6 Paragraph 1]; “After multi-class classification for anomaly detection: diagnosis is performed based on previous failure data knowledge of the estimated class, so the link of data to failure type is directly obtained from model [14, 21]. Once the possible failure type has been detected, semi-quantitative and qualitative approaches can be used by harnessing expert knowledge to evaluate its potential consequences, using tools such as FMEA [38] or Ishikawa diagram [137]. In addition, interpreting directly explainable models [2, 9] or using explainability on less interpretable models such as SVM [40] can also help to perform this task.” wherein the estimated class output is analyzed and diagnosed including evaluation of its failure type and potential consequences, thus reading on obtained data including a generated risk estimation related to the physical assets being analyzed; wherein the risk estimation conducted by FMEA (Failure Mode and Effects Analysis) tools and its associated Risk Priority Number (RPN) score calculated based on severity, occurrence and detectability of failure modes thus reads on an explicit risk estimation scores associated with risk estimation of the assets)
It would have been obvious to modify Bukhsh’s risk estimation of physical assets obtained during its artificial intelligent analysis to be associated with an explicit risk estimation score such as Serradilla’s FMEA tools and associated Risk Priority Numbers. One would have been motivated to do so because with the use of risk estimation scores, “the model can directly map the new data to the corresponding failure type automatically. However, companies that lack this type of data can only model normality using OCC models or must even use an unsupervised approach to model unlabelled data” (Serradilla [Section 5.3 Paragraph 3]).
Regarding Claim 5,
The combination of Bukhsh/Serradilla/Chu teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). The combination further discloses wherein the automatically selecting the maintenance solution pipeline is further based on asset interdependencies of the plurality of physical assets (Bukhsh [Page 42 Section “Asset Monitoring]; “Condition assessment of structures relies heavily on visual inspections due to its affordability and nondestructiveness. Since visual inspections are susceptible to subjective judgments of inspectors, the different types of sensors are instrumented on the structure to record various parameters. The example of a few sensors and recorded parameter include temperature sensors, strain gauges, inclinometers to measure angle and slope, string pots to gauge linear position and velocity, and accelerometers. Depending on the geometry of assets, other monitoring instruments such as unmanned aerial vehicles, laser scanning, and measurement machines can be used” wherein the condition-based maintenance of structure comprising a plurality of assets thus reads on a plurality of interdependent physical assets; wherein the optimization of the maintenance solution pipeline and associated model development according to the use-case of assets thus reads on such selection being based at least in part on such asset interdependencies)
Regarding Claim 6,
The combination of Bukhsh/Serradilla/Chu teaches the method of Claim 5 (and thus the rejection of Claim 5 is incorporated). The combination further discloses wherein the automatically selecting the maintenance solution pipeline is further based on a problem definition and is defined for a selected time period (Bukhsh [Figure 2];
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Regarding Claim 9,
The combination of Bukhsh/Serradilla/Chu teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). The combination further discloses regenerating an existing maintenance solution pipeline based on one or more updated constraints (Serradilla [Page 5 Section 2.2.4]; “Once an anomaly has been detected, the next stage consists of diagnosing whether this anomaly belongs to a faulty working condition and can evolve into a future failure or, in contrary, there is no risk of failure. The last case indicates that the anomaly detection model has not worked properly and therefore it may need to be reevaluated or retrained. The diagnosis is usually based on root cause analysis (RCA) techniques, which aim to identify the true cause of a problem. The diagnosis algorithm has to be suitable for the problem being addressed. There are several approaches to tackle this step, which depend on the implemented AD method and training data characteristics: multi-class classification, binary classification, one-class classification and clustering. Concretely these are chosen if the dataset has multiple failure types, failure and non failure observations, only observations of one class or unsupervised, respectively. There is another technique that commonly complements RCA: anomaly deviation quantification by health index (HI). It aims to measure assets’ damage by comparing current working data with historical data in a supervised or unsupervised way. It can either indicate a percentage of deviation with regard to normal working data, or show degradation level in a numerical scale, where the higher the value the more damaged the component is, where minimum value means no damage, maximum is fully damaged or failure and intermediate values indicate different degrees of degradation [119]” wherein the retrained anomaly detection model step of the PdM maintenance solution pipeline based on updated AD method and training data characteristics deemed to be more suitable for the problem being addressed reads on regeneration of an existing maintenance solution pipeline based on updated constraints)
Regarding Claim 10,
The combination of Bukhsh/Serradilla/Chu teaches the method of Claim 9 (and thus the rejection of Claim 9 is incorporated). The combination further discloses wherein the regenerating is further based on one or more updated objectives (Serradilla [Page 5 Section 2.2.4]; “Once an anomaly has been detected, the next stage consists of diagnosing whether this anomaly belongs to a faulty working condition and can evolve into a future failure or, in contrary, there is no risk of failure. The last case indicates that the anomaly detection model has not worked properly and therefore it may need to be reevaluated or retrained. The diagnosis is usually based on root cause analysis (RCA) techniques, which aim to identify the true cause of a problem. The diagnosis algorithm has to be suitable for the problem being addressed. There are several approaches to tackle this step, which depend on the implemented AD method and training data characteristics: multi-class classification, binary classification, one-class classification and clustering. Concretely these are chosen if the dataset has multiple failure types, failure and non failure observations, only observations of one class or unsupervised, respectively. There is another technique that commonly complements RCA: anomaly deviation quantification by health index (HI). It aims to measure assets’ damage by comparing current working data with historical data in a supervised or unsupervised way. It can either indicate a percentage of deviation with regard to normal working data, or show degradation level in a numerical scale, where the higher the value the more damaged the component is, where minimum value means no damage, maximum is fully damaged or failure and intermediate values indicate different degrees of degradation [119]” wherein the regeneration of the maintenance solution pipeline by retraining the anomaly detection portion of the pipeline is based on updated objectives regarding RCA and anomaly deviation quantification by health index (regeneration performed based on RCA indicating true cause is not anomalous and HI indicating assets are not degraded, thus demonstrating anomaly detection malfunctioned and necessitates regeneration)
Claims 11, 12 and 15 recite a system to perform the method of Claims 1, 5 and 9. Thus, Claims 11, 12 and 15 are rejected for reasons set forth in the rejection of Claims 1, 5 and 9.
Claims 16, 17 and 20 recite a computer program product comprising a compute readable storage media and stored program instructions to perform the method of Claims 1, 5 and 9. Thus, Claims 16, 17 and 20 are rejected for reasons set forth in the rejection of Claims 1, 5 and 9.
Claims 7-8; 13-14; and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Bukhsh et al. (“Predictive Maintenance for Infrastructure Asset Management” [2020], hereinafter “Bukhsh”) in view of Serradilla et al. (“Deep learning models for predictive maintenance: a survey, comparison, challenges and prospect” [2020], hereinafter “Serradilla”) in view of Chu et al. (US20180096261A1, hereinafter “Chu”) in view of Faller et al. (“Combining Condition Monitoring and Predictive Modeling to Improve Equipment Uptime on Drilling Rigs” [2008], hereinafter “Faller”).
Regarding Claim 7,
The combination of Bukhsh/Serradilla/Chu teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). The combination fails to explicitly disclose but Faller discloses automatically selecting the maintenance solution pipeline comprises traversing a tree structure to select the maintenance solution pipeline (Faller [Page 3 Section “Decision Tree Analysis”]; “A decision tree is a logical model represented as a binary (two-way split) tree that shows how the value of a target variable can be predicted by using the values of a set of predictor variables.
A condition based monitoring sensor network generates a lot of data from multiple technologies or channels. Each component or equipment creates large amounts of relevant data (predictor values), in similar time frames. Managing high data rates and recognizing patterns of interest in multi-channel data is a challenging problem.
Decision tree analysis organizes the large volume of predictor values from the CBM system. Decision trees assess the data and finds patterns based on conditions of interest. By various means, the process "learns" how to model (predict) the value of the target variable based on the predictor variables. It leverages machine-learning technology for detecting patterns in multi-channel time-series data. It determines interrelationships among the patterns and target values, which are then used to build Predictive models. By finding patterns, groupings or other ways to characterize the data, the expert software builds predictive models about equipment health. Decision trees are used to make inferences that help understand the purpose and results of the model.
A CBM/PdM system based on decision tree analysis builds profiles of normalcy under varying operating conditions enabling detection of potential failures based on patterns created by multiple ‘conditions of interest’ target values.”
Faller [Page 4 Section “Predictive Modeling”]; “Predictive modeling draws from statistics, machine learning, database techniques, pattern recognition, and optimization techniques. Predictive modeling based on decision tree analysis incorporates the process of extracting accurate and previously unknown information from large volumes of data.
Decision-tree-based modeling techniques produce models with interpretable structures, making them highly amenable to explanation and human inspection. This characteristic allows end users and analysts to understand the implications of the models and to take actions based on these implications.
Predictive Maintenance relies on the development of an asset strategy that determines the level of downtime necessary to maintain an asset, along with the resource structure required for organizing and controlling the work. Predictive maintenance analyzes and compares sampled data to reference models to assess the potential for failure.” wherein a sensor network collecting data by which a decision tree is utilized for analysis of the sensor data to determine, through predictive modeling, potential for failure across varying operating condition patterns and profiles of normalcy for end users to take appropriate actions based on such implications reads on automated selection of a maintenance solution pipeline (appropriate actions based on the decision tree risk analysis) comprising traversal of a tree structure)
It would have been obvious to modify Bukhsh/Serradilla/Chu’s maintenance solution pipeline selection through artificial intelligence processes to use specifically decision trees as the artificial intelligence processes for generating risk estimates of physical assets. One would have been motivated to do so because “A great advantage of decision trees over classical regression and neural networks is they are easy to interpret into actionable items with a clear understanding of how and why a downtime incident is avoided.” (Faller [Abstract Column 2 Line 29]).
Regarding Claim 8,
The combination of Bukhsh/Serradilla/Chu/Faller teaches the method of Claim 7 (and thus the rejection of Claim 7 is incorporated). The combination already discloses wherein the tree structure comprises a directed acyclic graph (Faller [Page 3 Section “Decision Tree Analysis”]; “A decision tree is a logical model represented as a binary (two-way split) tree that shows how the value of a target variable can be predicted by using the values of a set of predictor variables.
A condition based monitoring sensor network generates a lot of data from multiple technologies or channels. Each component or equipment creates large amounts of relevant data (predictor values), in similar time frames. Managing high data rates and recognizing patterns of interest in multi-channel data is a challenging problem.
Decision tree analysis organizes the large volume of predictor values from the CBM system. Decision trees assess the data and finds patterns based on conditions of interest. By various means, the process "learns" how to model (predict) the value of the target variable based on the predictor variables. It leverages machine-learning technology for detecting patterns in multi-channel time-series data. It determines interrelationships among the patterns and target values, which are then used to build Predictive models. By finding patterns, groupings or other ways to characterize the data, the expert software builds predictive models about equipment health. Decision trees are used to make inferences that help understand the purpose and results of the model” wherein a decision tree structure inherently comprises a directed acyclic graph)
Claims 13 and 14 recite a system to perform the method of Claims 7-8. Thus, Claims 13 and 14 are rejected for reasons set forth in the rejection of Claims 7-8.
Claims 18 and 19 recite a computer program product comprising a compute readable storage media and stored program instructions to perform the method of Claims 7-8. Thus, Claims 18 and 19 are rejected for reasons set forth in the rejection of Claims 7-8.
Response to Arguments
The Examiner acknowledges the Applicant’s amendments in which Claims 1, 11 and 16 are amended.
Applicant’s arguments regarding the 35 U.S.C. § 103 rejections of Claims 1-20 of the previous office action have been considered, but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
“Intelligent Dynamic Condition-Based Infrastructure Maintenance Scheduling” (US20240103959A1) which discloses maintenance pipeline scheduling for interdependent assets through predictive models
“Systems and Methods for Distributed Systemic Anticipatory Industrial Asset Intelligence” (US20200067789A1) which discloses real-time assessment of asset condition to determine maintenance solutions
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN J KIM whose telephone number is (571) 272-0523. The examiner can normally be reached 9-6.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matt Ell can be reached on (571) 270-3264. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JONATHAN J KIM/Examiner, Art Unit 2141
/MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141
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