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 .
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 .
Notice to Applicant
Claims 1- 20 have been examined in this application. This communication is the first action on the merits. Information Disclosure Statement (IDS) filed 7/12/2024 is acknowledged.
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 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-20 are directed to predicting remaining useful life of industrial systems.
Claim 1 recites a method for predicting remaining useful life of industrial systems, and Claim 13 recites an apparatus for predicting remaining useful life of industrial systems, which include monitoring the industrial equipment to sense historical time-series data associated with the industrial equipment using at least one sensor; storing the historical time-series data from the at least one sensor; accessing the historical time-series data and pre-processing the historical time-series data to extract higher-level features associated with the remaining useful life of the industrial equipment; and applying a jointly trained health predictor to the higher-level features to determine a prediction for the remaining useful life of the industrial equipment.
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea grouping of “Mental Processes” – evaluation. The recitation of “computing device”, “machine-readable memory”, “processor “ and “sensor module”, provide nothing in the claim elements to preclude the step from being “Mental Processes”- evaluation. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims primarily recite the additional element of using computer components to perform each step. The “computing device”, “machine-readable memory”, “processor “ and “sensor module” is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a computer component. See MPEP 2106.05(f). The additional element of “sensor”- is MPEP 2106.05(h) field of use.
Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also fail to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, and/or an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See 84 Fed. Reg. 55. In particular, there is a lack of improvement to a computer or technical field in prediction analysis.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “computing device”, “machine-readable memory”, “processor “ and “sensor module” is insufficient to amount to significantly more. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional element of “sensor”- is field of use.
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. With regards to receiving data and step 2B, it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information) and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Examiner concludes that the additional elements in combination fail to amount to significantly more than the abstract idea based on findings that each element merely performs the same function(s) in combination as each element performs separately. The claim is not patent eligible. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Dependent Claims 2-12, and 14-20 recite wherein the jointly trained health predictor is trained using real data and augmented with generated data; wherein the generated data is generated using a Generative Adversarial Network (GAN); wherein the jointly trained health predictor includes a neural network layer; wherein the neural network layer is a long short-term memory (LSTM) layer ; wherein the industrial equipment is comprised of industrial mechanical equipment ; wherein the industrial equipment comprises a bearing; wherein the bearing is a roller element bearing; wherein the historical time-series data further comprises shaft rotation speed and loading conditions associated with the roller element bearing; wherein the historical time-series data comprises vibration data and the at least one sensor comprises an accelerometer; A sensor module comprising the at least one sensor, the computing device, and the non-transitory machine readable memory and configured for performing the method;
a battery disposed within the housing and wherein the sensor module is powered by the battery; and further narrowing the abstract idea. These recited limitations in the dependent claims do not amount to significantly more than the above-identified judicial exceptions in Claims 1 and 13. Regarding Claims, 11, and the additional elements of “processor” and “machine readable memory” it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information). Regarding claims 10-12 and 20 and the additional element of “sensor” – it’s M2106.05(h). Regarding claim 3-5 and claim 15-17 and the additional element of Generative Adversarial Network and neural network layer - the specification discloses the machine learning at a high-level of generality, providing examples of different techniques that may be applied. The general use of a machine learning technique does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the machine learning is solely used a tool to perform the instructions of the abstract idea.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-6 and 11 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Maher, US Publication No. 20230281439 A1 (hereinafter Maher) in view of Kartikeya, US Publication No. 20230376398 A1 (hereinafter Kartikeya)
Regarding Claim 1,
Maher teaches
A method for using time-series data to…in cases where limited time-series training data is available, the method comprising: monitoring the industrial equipment to sense historical time-series data associated with the industrial equipment using at least one sensor; (Maher ,Par. 24 – The methods and devices of the present disclosure may address one or more of these deficiencies of conventional solutions. In some embodiments, one or more machine learning models associated with a processing chamber are to be trained. In some embodiments, the training data includes time trace data, e.g., sensor data. In some embodiments, a limited amount of training data is available. In some embodiments, a limited amount of one or more types of training data is available, e.g., data indicative of an impending fault in various subsystems, etc. In some embodiments, one or more machine learning models (e.g., an ensemble model including several models in parallel) may be used to generate synthetic time trace training data.; Par. 25- Synthetic time trace data may be generated using a machine learning model. In some embodiments, a relatively small volume of true data (e.g., data collected by sensors during a processing run, measured sensor time series data) may be used to train a model to generate synthetic time trace data. The generator model may be configured to generate synthetic data that matches distribution of the true data, e.g., that is statistically similar to the true data.; Par. 45)
storing the historical time-series data from the at least one sensor; (Maher ,Par. 42-43 – In some embodiments, corrective action component 122 obtains sensor data 142 (e.g., current sensor data 146) associated with manufacturing equipment 124 (e.g., from data store 140, etc.) and provides sensor data 142 (e.g., current sensor data 146) associated with the manufacturing equipment 124 to predictive system 110. In some embodiments, corrective action component 122 stores sensor data 142 in data store 140 and predictive server 112 retrieves sensor data 142 from data store 140. In some embodiments, predictive server 112 may store output (e.g., predictive data 168) of the trained model(s) 190 in data store 140 and client device 120 may retrieve the output from data store 140.)
accessing the historical time-series data and pre-processing the historical time-series data to extract higher-level features associated with the remaining useful life of the industrial equipment; (Maher ,Par. 56 – Sensor data 142 may include historical sensor data 144 and current sensor data 146. Sensor data may include sensor data time traces over the duration of manufacturing processes, associations of data with physical sensors, pre-processed data, such as averages and composite data, and data indicative of sensor performance over time (i.e., many manufacturing processes). Manufacturing parameters 150 and metrology data 160 may contain similar features. Historical sensor data 144 and historical manufacturing parameters may be historical data (e.g., at least a portion of these data may be used for training model 190). Current sensor data 146 may be current data (e.g., at least a portion to be input into learning model 190, subsequent to the historical data) for which predictive data 168 is to be generated (e.g., for performing corrective actions). Synthetic sensor data 162 may include data including representative features of several different data, e.g., may include features of old sensor data 148 (e.g., sensor data generated before training model 190) and features of new sensor data 149 (e.g., sensor data generated after training model 190).)
and applying a jointly trained health predictor to the higher-level features using a computing device by executing a set of instructions from a non-transitory machine-readable memory using a processor of the computing device to determine a prediction for the remaining useful life of the industrial equipment (Maher Par. 54-56- In some embodiments, predictive component 114 receives current sensor data 146 and/or current manufacturing parameters 154, performs signal processing to break down the current data into sets of current data, provides the sets of current data as input to a trained model 190, and obtains outputs indicative of predictive data 168 from the trained model 190. In some embodiments, predictive data is indicative of metrology data (e.g., prediction of substrate quality). In some embodiments, predictive data is indicative of component health. In some embodiments, predictive data is indicative of processing progress (e.g., utilized to end a processing operation).
In some embodiments, the various models discussed in connection with model 190 (e.g., supervised machine learning model, unsupervised machine learning model, etc.) may be combined in one model (e.g., an ensemble model), or may be separate models. Predictive component 114 may receive current sensor data 146 and current manufacturing parameters 154, provide the data to a trained model 190, and receive information indicative of how much several components in the manufacturing chamber have drifted from their previous performance. Data may be passed back and forth between several distinct models included in model 190 and predictive component 114. In some embodiments, some or all of these operations may instead be performed by a different device, e.g., client device 120, server machine 170, server machine 180, etc. It will be understood by one of ordinary skill in the art that variations in data flow, which components perform which processes, which models are provided with which data, and the like are within the scope of this disclosure.”;Par.136).
Maher teaches machine learning analysis and the feature is expounded upon by Kartikeya:
…predict remaining useful life of industrial equipment... (Kartikeya Abstract-“ Some embodiments are associated with a system and method for deep learning unsupervised remaining useful life (RUL) prediction in Internet of Things (IoT) sensor networks or manufacturing execution systems. The system and method use multilevel discrete wavelet for raw data transformation and a bidirectional long short-term memory (BiLSTM) based autoencoder neural network for RUL prediction .)
Maher and Kartikeya are directed to predictive analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Maher, as taught by Kartikeya, by utilizing additional machine learning analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Maher with the motivation of improving remaining useful life prediction (Kartikeya Par. 6).
Regarding Claim 2 and Claim 14, Maher in view of Kartikeya teach The method of claim 1… and The sensor module of claim 13…
wherein the jointly trained health predictor is trained using real data and augmented with generated data (Maher Par. 24-25- The methods and devices of the present disclosure may address one or more of these deficiencies of conventional solutions. In some embodiments, one or more machine learning models associated with a processing chamber are to be trained. In some embodiments, the training data includes time trace data, e.g., sensor data. In some embodiments, a limited amount of training data is available. In some embodiments, a limited amount of one or more types of training data is available, e.g., data indicative of an impending fault in various subsystems, etc. In some embodiments, one or more machine learning models (e.g., an ensemble model including several models in parallel) may be used to generate synthetic time trace training data. Par. 57-58) .
Regarding Claim 3 and Claim 15, Maher in view of Kartikeya teach The method of claim 2… and The sensor module of claim 14…
wherein the generated data is generated using a Generative Adversarial Network (GAN) (Maher Par. 27- In some embodiments, generation of synthetic data may include the use of a generative adversarial network (GAN). A GAN is a type of unsupervised (e.g., training input is provided to the model without providing a target output during training operations) machine learning model. A basic GAN includes two parts: a generator and a discriminator. The generator produces synthetic data, e.g., time trace sensor data.) .
Regarding Claim 4 and Claim 16, Maher in view of Kartikeya teach The method of claim 1… and The sensor module of claim 13…
wherein the jointly trained health predictor includes a neural network layer. (Maher Par. 53- One type of machine learning model that may be used to perform some or all of the above tasks is an artificial neural network, such as a deep neural network. Artificial neural networks generally include a feature representation component with a classifier or regression layers that map features to a target output space. A convolutional neural network (CNN), for example, hosts multiple layers of convolutional filters. Pooling is performed, and non-linearities may be addressed, at lower layers, on top of which a multi-layer perceptron is commonly appended, mapping top layer features extracted by the convolutional layers to decisions (e.g. classification outputs). A recurrent neural network (RNN) is another type of machine learning model. A recurrent neural network model is designed to interpret a series of inputs where inputs are intrinsically related to one another, e.g., time trace data, sequential data, etc.) .
Regarding Claim 5 and Claim 17, Maher in view of Kartikeya teach The method of claim 4… and The sensor module of claim 14…
Mayer teaches machine learning modelling and the feature is expounded by Kartikeya:
wherein the neural network layer is a long short-term memory (LSTM) layer. (Kartikeya Par. 39- Long short-term memory (LSTM)” is an artificial neural network used in the fields of artificial intelligence and deep learning. Unlike standard feedforward neural networks, LSTM has feedback connections. It is a variety of recurrent neural networks (RNNs) that are capable of learning long-term dependencies, especially in sequence prediction problems.) .
Maher and Kartikeya are directed to predictive analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Maher, as taught by Kartikeya, by utilizing additional machine learning analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Maher with the motivation of improving remaining useful life prediction (Kartikeya Par. 6).
Regarding Claim 6, Maher in view of Kartikeya teach The method of claim 1…
wherein the industrial equipment is comprised of industrial mechanical equipment. (Maher Par. 14- Manufacturing equipment is used to produce products, such as substrates (e.g., wafers, semiconductors). Manufacturing equipment may include a manufacturing or processing chamber to separate the substrate from the environment. The properties of produced substrates are to meet target values to facilitate specific functionalities. Manufacturing parameters are selected to produce substrates that meet the target property values. Many manufacturing parameters (e.g., hardware parameters, process parameters, etc.) contribute to the properties of processed substrates. Manufacturing systems may control parameters by specifying a set point for a property value and receiving data from sensors disposed within the manufacturing chamber, making adjustments to the manufacturing equipment until the sensor readings match the set point. In some embodiments, trained machine learning models are utilized to improve performance of manufacturing equipment.)
Regarding Claim 11, Maher in view of Kartikeya teach The method of claim 1…
A sensor module comprising the at least one sensor, the computing device, and the non-transitory machine readable memory and configured for performing the method of claim 1. (Maher Par. 5-6; Par. 14- Manufacturing parameters are selected to produce substrates that meet the target property values. Many manufacturing parameters (e.g., hardware parameters, process parameters, etc.) contribute to the properties of processed substrates. Manufacturing systems may control parameters by specifying a set point for a property value and receiving data from sensors disposed within the manufacturing chamber, making adjustments to the manufacturing equipment until the sensor readings match the set point. In some embodiments, trained machine learning models are utilized to improve performance of manufacturing equipment.
Regarding Claim 13,
Maher teaches
A sensor module for predicting remaining useful life of industrial equipment in an industrial environment, the sensor comprising: a sensor housing; a processor disposed within the sensor housing; and at least one sensor for sensing machine data for the industrial equipment, the at least one sensor operatively connected to the processor; wherein the processor is configured to:…; (Maher Par. 5-6; Par. 14 Par. 24 – The methods and devices of the present disclosure may address one or more of these deficiencies of conventional solutions. In some embodiments, one or more machine learning models associated with a processing chamber are to be trained. In some embodiments, the training data includes time trace data, e.g., sensor data. In some embodiments, a limited amount of training data is available. In some embodiments, a limited amount of one or more types of training data is available, e.g., data indicative of an impending fault in various subsystems, etc. In some embodiments, one or more machine learning models (e.g., an ensemble model including several models in parallel) may be used to generate synthetic time trace training data.; Par. 25- Synthetic time trace data may be generated using a machine learning model. In some embodiments, a relatively small volume of true data (e.g., data collected by sensors during a processing run, measured sensor time series data) may be used to train a model to generate synthetic time trace data. The generator model may be configured to generate synthetic data that matches distribution of the true data, e.g., that is statistically similar to the true data.; Par. 45)
extract higher-level features associated with the remaining useful life of the industrial equipment from the machine data; (Maher ,Par. 56 – Sensor data 142 may include historical sensor data 144 and current sensor data 146. Sensor data may include sensor data time traces over the duration of manufacturing processes, associations of data with physical sensors, pre-processed data, such as averages and composite data, and data indicative of sensor performance over time (i.e., many manufacturing processes). Manufacturing parameters 150 and metrology data 160 may contain similar features. Historical sensor data 144 and historical manufacturing parameters may be historical data (e.g., at least a portion of these data may be used for training model 190). Current sensor data 146 may be current data (e.g., at least a portion to be input into learning model 190, subsequent to the historical data) for which predictive data 168 is to be generated (e.g., for performing corrective actions). Synthetic sensor data 162 may include data including representative features of several different data, e.g., may include features of old sensor data 148 (e.g., sensor data generated before training model 190) and features of new sensor data 149 (e.g., sensor data generated after training model 190).)
apply a jointly trained health predictor to the higher-level features using a computing device by executing a set of instructions from a non-transitory machine readable memory using the processor to determine a prediction for the remaining useful life of the industrial equipment (Maher Par. 54-56- In some embodiments, predictive component 114 receives current sensor data 146 and/or current manufacturing parameters 154, performs signal processing to break down the current data into sets of current data, provides the sets of current data as input to a trained model 190, and obtains outputs indicative of predictive data 168 from the trained model 190. In some embodiments, predictive data is indicative of metrology data (e.g., prediction of substrate quality). In some embodiments, predictive data is indicative of component health. In some embodiments, predictive data is indicative of processing progress (e.g., utilized to end a processing operation).
In some embodiments, the various models discussed in connection with model 190 (e.g., supervised machine learning model, unsupervised machine learning model, etc.) may be combined in one model (e.g., an ensemble model), or may be separate models. Predictive component 114 may receive current sensor data 146 and current manufacturing parameters 154, provide the data to a trained model 190, and receive information indicative of how much several components in the manufacturing chamber have drifted from their previous performance. Data may be passed back and forth between several distinct models included in model 190 and predictive component 114. In some embodiments, some or all of these operations may instead be performed by a different device, e.g., client device 120, server machine 170, server machine 180, etc. It will be understood by one of ordinary skill in the art that variations in data flow, which components perform which processes, which models are provided with which data, and the like are within the scope of this disclosure.”;Par.136).
Maher teaches machine learning analysis and the feature is expounded upon by Kartikeya:
…predict remaining useful life of industrial equipment... (Kartikeya Abstract-“ Some embodiments are associated with a system and method for deep learning unsupervised remaining useful life (RUL) prediction in Internet of Things (IoT) sensor networks or manufacturing execution systems. The system and method use multilevel discrete wavelet for raw data transformation and a bidirectional long short-term memory (BiLSTM) based autoencoder neural network for RUL prediction .)
Maher and Kartikeya are directed to predictive analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Maher, as taught by Kartikeya, by utilizing additional machine learning analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Maher with the motivation of improving remaining useful life prediction (Kartikeya Par. 6).
Claims 7-10 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Maher, US Publication No. 20230281439 A1 (hereinafter Maher) in view of Kartikeya, US Publication No. 20230376398 A1 (hereinafter Kartikeya) and in further view of Wang et al, “A Probabilistic Framework for Remaining Useful Life Prediction of Bearings”, 2021, IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 70, pp 1-12 (hereinafter Wang).
Regarding Claim 7 and Claim 18, Maher in view of Kartikeya teach The method of claim 1… and The sensor module of claim 13…
Maher in view of Kartikeya fail to teach the feature taught by Wang:
wherein the industrial equipment comprises a bearing. (Wang, sec VII. A – Sensor data including RPM velocity/”shaft rotation speed” and loading conditions with the roller bearings.; Sec IV-Exponential growth model (EGM) is one of the most popular models in model based prediction. EGM was first applied in population growth study and has been demonstrated effective in RUL prediction of mechanical components, such as rolling element bearings [17] and aluminum alloy compact [29]. )
Maher, Kartikeya and Wang are directed to predictive analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Maher in view of Kartikeya, as taught by Wang, by utilizing specific industrial equipment with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Maher in view of Kartikeya with the motivation of improving effective in RUL prediction of mechanical components, such as rolling element
bearings (Wang Sec IV).
Regarding Claim 8 and Claim 19, Maher in view of Kartikeya in further view of Wang teach The method of claim 1… and The sensor module of claim 13…
Maher in view of Kartikeya fail to teach the feature taught by Wang:
wherein the bearing is a roller element bearing (Wang, sec VII. A – Sensor data including RPM velocity/”shaft rotation speed” and loading conditions with the roller bearings.; Sec IV-Exponential growth model (EGM) is one of the most popular models in model based prediction. EGM was first applied in population growth study and has been demonstrated effective in RUL prediction of mechanical components, such as rolling element bearings [17] and aluminum alloy compact [29]. )
Maher, Kartikeya and Wang are directed to predictive analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Maher in view of Kartikeya, as taught by Wang, by utilizing specific industrial equipment with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Maher in view of Kartikeya with the motivation of improving effective in RUL prediction of mechanical components, such as rolling element bearings (Wang Sec IV).
Regarding Claim 9 Maher in view of Kartikeya in further view of Wang teach The method of claim 8 wherein the historical time-series data further comprises…
Maher in view of Kartikeya fail to teach the feature taught by Wang:
shaft rotation speed and loading conditions associated with the roller element bearing. (Wang, sec VII. A – Sensor data including RPM velocity/”shaft rotation speed” and loading conditions with the roller bearings.; Sec IV-Exponential growth model (EGM) is one of the most popular models in model based prediction. EGM was first applied in population growth study and has been demonstrated effective in RUL prediction of mechanical components, such as rolling element bearings [17] and aluminum alloy compact [29]. )
Maher, Kartikeya and Wang are directed to predictive analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Maher in view of Kartikeya, as taught by Wang, by utilizing specific industrial equipment with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Maher in view of Kartikeya with the motivation of improving effective in RUL prediction of mechanical components, such as rolling element bearings (Wang Sec IV).
Regarding Claim 10 and Claim 20 Maher in view of Kartikeya in further view of Wang teach The method of claim 6 wherein the historical time-series data comprises… and The sensor module of claim 13…
Maher in view of Kartikeya fail to teach the feature taught by Wang:
shaft rotation speed and loading conditions associated with the roller element bearing. (Wang, sec VII. A – Obtaining vibration signals/”machine sensor data” by using an accelerometer sensor.)
Maher, Kartikeya and Wang are directed to predictive analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Maher in view of Kartikeya, as taught by Wang, by utilizing specific industrial equipment with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Maher in view of Kartikeya with the motivation of improving effective in RUL prediction of mechanical components, such as rolling element bearings (Wang Sec IV).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Maher, US Publication No. 20230281439 A1 (hereinafter Maher) in view of Kartikeya, US Publication No. 20230376398 A1 (hereinafter Kartikeya) and in further view of in view of OnQ, “Developers: Building smarter edge computing solutions with smart sensors”, July 2021, Qualcomm Technologies, pp1-3 (hereinafter OnQ).
Maher in view of Kartikeya fail to teach the feature taught by OnQ:
sensor module comprising a housing, the at least one sensor, the computing device disposed within the housing, and the non-transitory machine readable memory disposed within the housing (OnQ, pg 1-3 – A smart sensor module with a housing, a computing device and memory for running software enclosed withing the housing.)
Maher, Kartikeya and OnQ are directed to sensor analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Maher in view of Kartikeya, as taught by OnQ, by utilizing specific industrial equipment with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Maher in view of Kartikeya with the motivation of increasing overall performance and enhance power efficiency by incorporating the above limitations, as suggested by OnQ (pg 1).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: US Patent No. 8301406B2 to Lee et al.- Abstract-“ A method of prognosing a mechanical system to predict when a failure may occur is disclosed. Measurement data corresponding to the mechanical system is used to extract one or more features by decomposing the measurement data into a feature space. A prediction model is then selected from a plurality of prediction models for the one or more features based at least on part on a degradation status of the mechanical system and a reinforcement learning model. A predicted feature space is generated by applying the selective prediction model to the feature space as well as a confidence value by comparing the predicted feature space with a normal baseline distribution, a faulty baseline distribution, or a combination thereof. A status of mechanical system based at least in part on the confidence value is then provided.”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Chesiree Walton, whose telephone number is (571) 272-5219. The examiner can normally be reached from Monday to Friday between 8 AM and 5 PM. If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Patricia Munson, can be reached at (571) 270-5396. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
Another resource that is available to applicants is the Patent Application Information Retrieval (PAIR). Information regarding the status of an application can be obtained from the (PAIR) system. Status information for published applications may be obtained from either Private PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, please feel free to contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
Applicants are invited to contact the Office to schedule an in-person interview to discuss and resolve the issues set forth in this Office Action. Although an interview is not required, the Office believes that an interview can be of use to resolve any issues related to a patent application in an efficient and prompt manner.
Sincerely,
/CHESIREE A WALTON/ Examiner, Art Unit 3624
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