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. Claim Rejections - 35 USC § 112 The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 1 , 12, and 17 discloses “ using a machine- learning model pre-trained to predict normal operational behaviour .” Claim 5 discloses “ linear regression models is provided respectively to predict normal operational behaviour .” It is unclea r whether the neural network predicting behaviour is the same as machine learning predicting behaviour. The applicant can overcome this rejection by clearly delineating the two. Claim 1 is further not clear because it is un clear how the standardized residual can be calculated for the state variables based on a behaviour prediction. The applicant can overcome this rejection by clearly defining how this is calculated . Claim 1 is not clear because claim 1 specifies two outputs with respect to processing: "predict a normal operation" and "calculate a residual". Thus, it is un clear whether the processing steps performs both steps , and in this case in which order , or if only one of both steps is executed. The applicant can overcome this rejection by clearly depicting the steps and in what order, if necessary. Claim 1 is further not clear (Article 6 PCT) because it is not clear if the deviation is obtained from the standardized residual (as specified in the last feature how the standardized residual can be calculated for the state variables based on a behaviour prediction. The applicant can overcome this rejection by clearly defining how this is obtain ed. Claim 14 and Claim 20 disclose “ wherein the artificial neural network is one of a convolutional neural network and a long term short term memory neural network. ” The term “one of” contradicts the term “and.” It is unclear whether the neural network is either CNN, LTSTMNN or both. The applicant can overcome this rejection by clearly delineating whether one or both is required. Claim s 12 and 18 recites the limitation " FILLIN "Enter appropriate information" \* MERGEFORMAT indicating any overshooting or undershooting " in FILLIN "Enter appropriate information" \* MERGEFORMAT second limitation . There is insufficient antecedent basis for this limitation in the claim. Claims 18 recites the limitation " FILLIN "Enter appropriate information" \* MERGEFORMAT to train the artificial neural network " in FILLIN "Enter appropriate information" \* MERGEFORMAT the last limitation . There is insufficient antecedent basis for this limitation in the claim. 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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Specifically, representative Claim 1 recites: A computer-implemented method for monitoring operation of a machine having a mechanical component, the method comprising: receiving sensor data comprising a time series of measurements of an operational parameter of the machine corresponding to a state variable ; processing the time series of measurements for the state variable using a machine-learning model pre-trained to predict normal operational behaviour of the machine based on values of the state variable observed for a time period during normal operation of the machine, the processing to calculate a standardized residual for the state variable across the time series based on a prediction of the pre-trained machine-learning model; and identifying any deviation from normal operation of the machine based on values of the standardized residual . The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”. Under the Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (process). Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim limitation, that covers mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) and mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion. For example, steps of “ processing the time series of measurements for the state variable using a machine-learning model pre-trained to predict normal operational behaviour of the machine based on values of the state variable observed for a time period during normal operation of the machine, the processing to calculate a standardized residual for the state variable across the time series based on a prediction of the pre-trained machine-learning model; and identifying any deviation from normal operation of the machine based on values of the standardized residual ” are treated as belonging to mental process grouping . Similar limitations comprise the abstract ideas of Claims 12, 17 and 18. Next, under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application. In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception. The above claims comprise the following additional elements: In Claim 12: artificial neural network, machine In Claim 17: data processing apparatus, memory, processing circuitry, machine In Claim 18: data processing apparatus, memory, processing circuitry, machine The additional element of “ artificial neural network, a machine, data processing apparatus, memory, processing circuitry” are generally recited and are not qualified as particular machines. Further, the limitation of “ receiving sensor data comprising a time series of measurements of an operational parameter of the machine corresponding to a state variable,” is considered by MPEP 2106.05(g) as insignificant extra-solution activity, mere data gathering. In conclusion, the above additional elements, considered individually and in combination with the other claim elements do not reflect an improvement to other technology or technical field, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B. However, the above claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B analysis). The claims, therefore, are not patent eligible. With regards to the dependent claims, claims 2 -11, 13-16 and 19 provide additional features/steps which are part of an expanded algorithm, so these limitations should be considered part of an expanded abstract idea of the independent claims. Claim Rejections - 35 USC § 102 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. Claims FILLIN "Insert the claim numbers which are under rejection." \d "[ 1 ]" 1-5, 16 and 17 are rejected under 35 U.S.C. 102 FILLIN "Insert either \“(a)(1)\” or \“(a)(2)\” or both. If paragraph (a)(2) of 35 U.S.C. 102 is applicable, use form paragraph 7.15.01.aia, 7.15.02.aia or 7.15.03.aia where applicable." \d "[ 2 ]" (a)(1) as being FILLIN "Insert either—clearly anticipated—or—anticipated—with an explanation at the end of the paragraph." \d "[ 3 ]" anticipated by FILLIN "Insert the prior art relied upon." \d "[ 4 ]" M a zzaro et al. (US20130024179A1, 2013-01-24) herein referred to as Mazzaro . Regarding Claim 1, Mazzaro teaches a computer-implemented method for monitoring operation of a machine having a mechanical component (Abstract, Fig. 1-2) , the method comprising: receiving sensor data comprising a time series of measurements of an operational parameter of the machine corresponding to a state variable [0022] ; processing the time series of measurements for the state variable using a machine-learning model pre-trained to predict normal operational behaviour of the machine based on values of the state variable observed for a time period during normal operation of the machine [0023; 0037] , the processing to calculate a standardized residual for the state variable across the time series based on a prediction of the pre-trained machine-learning model [0024] ; and identifying any deviation from normal operation of the machine based on values of the standardized residual [0024; 0046] . Regarding Claim 2, Mazzaro teaches the computer-implemented method of claim 1, wherein the identification of the deviation from normal operation comprises taking into account a sign of the standardized residual such that an overshooting of the standardized residual is distinguishable from an under-shooting of the standardized residual [0024]. Regarding Claim 3, Mazzaro teaches the computer-implemented method of claim 2, wherein the received sensor data relates to a plurality of operational parameters of the machine corresponding to respective different state variables and wherein the identification of the deviation takes into account correlations between the standardized residuals of the plurality of operational parameters [0022, 0024]. Regarding Claim 4, Mazzaro teaches the computer-implemented method of claim 3, wherein a respective different pre-trained machine learning model is provided for each different state variable [0023]. Regarding Claim 5, Mazzaro teaches the computer-implemented method of claim 4, wherein an integer number, N, of linear regression models is provided respectively to predict normal operational behaviour for N state variables [0023]. Regarding Claim 16, Mazzaro teaches a transitory or non-transitory machine readable medium comprising machine-readable instructions to perform the computer-implemented method of claim 1 [0050]. Regarding Claim 17, Mazzaro teaches a data processing apparatus (Abstract) comprising: a memory to store sensor data captured during operation of a machine having a mechanical part [ 0037; 0050]; and processing circuitry [0023] arranged to: access the sensor data from the memory [0037] , wherein the sensor data comprises a time series of measurements of an operational parameter of the machine corresponding to a state variable [0022] ; process the time series of measurements for the state variable using a machine-learning model pre-trained to predict normal operational behaviour of the machine based on values of the state variable observed for a time period during normal operation of the machine [0023, 0037] , the processing to calculate a standardized residual for the state variable across the time series based on a prediction of the pre-trained machine-learning model [0024] ; and identify any deviation from normal operation of the machine based on values of the standardized residual [0024; 0046]. 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. Claim s 6 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over FILLIN "Insert the prior art reference(s) relied upon for the obviousness rejection." \d "[ 2 ]" Mazzaro as applied to claim FILLIN "Pluralize claim, if necessary, and then insert the claim number(s) which is/are under rejection." \d "[ 3 ]" s 1-5 and 16-17 above, and further in view of FILLIN "Insert the additional prior art reference(s) relied upon for the obviousness rejection." \d "[ 4 ]" Weizhong et al. (US2019219994A1, 2019-07-18) herein referred to as Weizhong . Regarding Claim 6, Maz z aro teaches t he computer-implemented method of claim 3 . Mazzaro further teaches receiving sensor data comprising time series data corresponding to a state variables [0023; 0037] and wherein the identification of the deviation from normal operation comprises taking into account a sign of the standardized residual such that an overshooting of the standardized residual is distinguishable from an under-shooting of the standardized residual [0024], but fails to specifically teach generating a machine-readable heatmap for the plurality of state variables across the time series, the heatmap to indicate for each state variable, any overshooting and any undershooting of the standardized residuals for at least one state variable and wherein the heatmap is used in the identification of the deviation from normal operation. However, in a related field, Weizhong discloses a heatmap as visualized information of anomaly analysis [ 0106; Fig. 19]. Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Mazzaro to incorporate the teachings of Weizhong by including: using a heatmap to visually depict the anomalies in order to better identify an anomaly or deviation from normal operation. Regarding Claim 7, the combination further teaches t he computer-implemented method of claim 6, further comprising generating a digital image representing the heatmap and presenting the digital image to a user on a control interface for the machine [0106; fig. 19]. Claims FILLIN "Pluralize claim, if necessary, and then insert the claim number(s) which is/are under rejection." \d "[ 1 ]" 8-11 are rejected under 35 U.S.C. 103 as being unpatentable over FILLIN "Insert the prior art reference(s) relied upon for the obviousness rejection." \d "[ 2 ]" Mazzaro and Weizhong as applied to claim FILLIN "Pluralize claim, if necessary, and then insert the claim number(s) which is/are under rejection." \d "[ 3 ]" s 6-7 above, and further in view of Jung et al. (KO20200034545A, 2020-03-31) herein referred to as Jung . Regarding Claim 8, the combination teaches t he computer-implemented method of claim 6 . The combination further teaches generating a digital image representing the heatmap and the use of a heatmap to identification of any deviation from normal operation (Weizhong: [ 0106 ] ; fig. 19 ) The combination fails to specifically teach providing the heatmap to an artificial neural network pre-trained using heatmaps for the plurality of state variables captured during normal operation of the machine, the identification of any deviation from normal operation being performed using the pre-trained artificial neural network. However, in a related field, Jung teaches providing the heatmap to an artificial neural network pre-trained using heatmaps for the plurality of state variables captured during normal operation of the machine, the identification of any deviation from normal operation being performed using the pre-trained artificial neural network (pg. 3, sixth and 11 th para.; pg. 5). Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Mazzaro and Weizhong to incorporate the teachings of Jung by including: a neural network in order to better identify an anomaly or deviation from normal operation. Regarding Claim 9, the combination teaches t he computer-implemented method of claim 8, wherein the artificial neural network is pre-trained based on the heatmaps for the plurality of state variables captured during normal operation to perform damage classification to identify different types of deviation from normal operation based on correlations in undershooting and overshooting as a function of time between different ones of the plurality of state variables ( Jung : pg. 3, sixth and 11 th para.; pg. 5 ; Mazzaro: [0022-0024; 0046] ) . Regarding Claim 10, the combination further teaches t he computer-implemented of claim 9, wherein the artificial neural network is pre-trained by segmenting a heatmap into a plurality of distinct or partially overlapping time segments in inputting the time-segmented heatmap images to the artificial neural network for classification ( Jung : pg. 6, para. 5 and 8; pg. 9; pg. 5; fig. 2-8). Regarding Claim 11, the combination further teaches t he computer-implemented method of claim 9, wherein the heatmap images used for pre-training are labeled by a known maintenance issue present in the machine when the sensor data for the heatmap image was captured ( Jung : pg. 5, para. 5 (Examiner’s Note: compared with reference image of three types of defect conditions (known)). Claims FILLIN "Pluralize claim, if necessary, and then insert the claim number(s) which is/are under rejection." \d "[ 1 ]" 12-15 are rejected under 35 U.S.C. 103 as being unpatentable over Jung (KO20200034545A, 2020-03-31) , further in view of Mazzaro. Regarding Claim 12, Jung teaches a computer-implemented method for training an artificial neural network to identify any deviations from normal operation of a machine having a mechanical part (pg. 5, para 4 and 5; CNN and defect of motor, stator or bearings) . Jung further teaches the method comprising: receiving machine-readable data calculated based on a difference in values of one or more state variables between sensor data captured from the machine in a time period and a prediction for the value of the corresponding state variable made using a pre-trained machine learning model (pg. 5 and 6) ; and using the heatmap data set to train the artificial neural network to detect any maintenance issues with the machine (pg. 3-5) . Jung fails to specifically teach data comprising standardized residuals and generating a heatmap data set representing the time period and indicating any overshooting or undershooting as a function of time of standardized residuals of sensor data for each of one or more state variables . However, in a related field, Mazzaro teaches standardized residuals and generating a heatmap data set representing the time period and indicating any overshooting or undershooting as a function of time of standardized residuals of sensor data for each of one or more state variables [0023-0024; 0037]. Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Jung to incorporate the teachings of Mazzaro by including: the limitations in order to better identify an anomaly or deviation from normal operation. Regarding Claim 13, the combination teaches t he computer-implemented method of claim 12, wherein the heatmap data set is rendered as image data and the heatmap image is input to the artificial neural network to perform the training ( Jung : Jung : pg. 3, sixth and 11 th para.; pg. 5 ). Regarding Claim 14, the combination teaches the computer-implemented method of claim 13, wherein the artificial neural network is one of a convolutional neural network and a long term short term memory neural network ( Jung : pg. 5). Regarding Claim 15, the combination further teaches t he computer-implemented method according to claim 13, wherein the heatmap is segmented in to a plurality of distinct or overlapping time segments prior to input to the artificial neural network to train the artificial neural network (pg. 5-6). Claims 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Mazzaro et al. (US20130024179A1, 2013-01-24) , further in view of over Jung et al. (KO20200034545A, 2020-03-31), herein referred to as Jung Regarding Claim 18, Mazzaro teaches a data processing apparatus [Abstract] comprising processing circuitry [0023] to: receive machine-readable data comprising standardized residuals calculated based on a difference in values of one or more state variables between sensor data captured from the machine in a time period and a prediction for the value of the corresponding state variable made using a pre-trained machine learning model [0024] ; generate a time period and indicating any overshooting or undershooting as a function of time of standardized residuals of sensor data for each of one or more state variables [0024]. Mazzaro fails to teach generating a heatmap data set representing a time period and indicating any overshooting or undershooting as a function of time of standardized residuals of sensor data for each of one or more state variables and use the heatmap data set to train the artificial neural network to detect any maintenance issues with the machine . However, in a related field, Jung teaches generating a heatmap representing a time period and use the heatmap data set to train the artificial neural network to detect any maintenance issues with the machine (pg. 3-5). Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Mazzaro to incorporate the teachings of Jung by including: the limitations in order to better identify an anomaly or deviation from normal operation. Regarding Claim 19, the combination teaches t he data processing apparatus of claim 18, wherein the heatmap data set is rendered as image data and the heatmap image is input to the artificial neural network to perform the training ( Jung : pg. 3-5). Regarding Claim 20, the combination teaches th e data processing apparatus according to claim 19, wherein the artificial neural network is one of a convolutional neural network and a long term short term memory neural network (pg. 5) Conclusion The prior art made record and not relied upon is considered pertinent to applicant’s disclosure. Abukwaik et al. ( METHOD FOR DETERMINING THE STATE OF HEALTH OF AN INDUSTRIAL PROCESS , 2023-05-24) teaches A method (100) for determining the state of health (1*) of an industrial process (1), wherein the process (1) is executed by at least one industrial plant comprising an arrangement of entities (2a-2f) and the state of each such entity (2a-2f) is characterized by a set of entity state variables (3a-3f), comprising the steps of:• obtaining (110) values, and/or time series of values, of the entity state variables (3a-3f);• for each entity (2a-2f), providing (120) the values (3a-3f), and/or time series of values (3a-3f), to a model (4a-4f) corresponding to the respective entity (2a-2f), thereby obtaining a prediction of the state of health (5a-5f) of the respective entity (2a-2f);• determining (130), based at least in part on the layout (1a) of the industrial plant executing the process (1), propagation paths (6) for anomalies between said entities (2a-2f);• determining (140), based at least in part on said propagation paths (6), importances (7a-7f) of the states of health (5a-5f) of the individual entities (2a-2f) for the overall state of health (1*) of the process; and• aggregating (150), based at least in part on said importances (7a-7f), the individual states of health (5a-5f) of the entities (2a-2f) to obtain the overall state of health (1*) of the process (1) ; Schmitt et al. ( METHOD AND SYSTEM FOR SEMI-SUPERVISED DEEP ANOMALY DETECTION FOR LARGE-SCALE INDUSTRIAL MONITORING SYSTEMS BASED ON TIME-SERIES DATA UTILIZING DIGITAL TWIN SIMULATION DATA , 2021-04-15) teaches a computer-implemented method for detecting an anomalous operating status of a technical system. A training phase obtains a first set of time-series values generated by a digital twin simulation of the technical system for a regular operating status and a second set of time-series values measured by sensors in an anomalous operating status, and adjusts parameters of a machine learning model for detecting the regular operating status and for discriminating data samples of the regular operating status from data samples of the anomalous operating status to generate a trained machine learning model. A monitoring phase obtains a set of multivariate time-series values measured by the sensors, calculates an anomaly score value for determining whether the technical system is in an anomalous operating status based on the obtained set of multi-variate time-series values and the trained machine learning model, and outputs a signal including information on the determined anomalous operating status ; Talyansky et al. ( AUTOMATIC ROOT CAUSE ANALYSIS OF FAILURES IN AUTONOMOUS VEHICLE , 2021-04-08) teaches automatically detecting failure root cause in an autonomous vehicle, by receiving sensor data captured during a period preceding the failure by sensor(s) deployed to sense an environment of the autonomous vehicle, analyzing the sensor data to identify object(s) in the environment, creating a failure scenario defining a time-lined motion pattern of each object, computing a feature vector comprising features extracted from an output generated by sub-systems of the autonomous vehicle during the failure scenario, applying to the feature vector machine learning classification model(s) trained with a plurality of labeled feature vectors computed for a plurality of failure scenarios and their corresponding success scenarios, identifying key features significantly contributing to an outcome of the trained machine learning classification model(s) by applying an interpretation model to the machine learning classification model(s), the feature vector(s) and/or the outcome and estimating root cause failure sub-system(s) according to its association with the key feature(s) ; Wang et al. ( COMPENSATING FOR OUT-OF-PHASE SEASONALITY MODES IN TIME-SERIES SIGNALS TO FACILITATE PROGNOSTIC-SURVEILLANCE OPERATIONS , 2020-11-26) teaches a system that performs seasonality-compensated prognostic-surveillance operations for an asset. During operation, the system obtains time-series sensor signals gathered from sensors in the asset during operation of the asset. Next, the system identifies seasonality modes in the time-series sensor signals. The system then determines frequencies and phase angles for the identified seasonality modes. Next, the system uses the determined frequencies and phase angles to filter out the seasonality modes from the time-series sensor signals to produce seasonality-compensated time-series sensor signals. The system then applies an inferential model to the seasonality-compensated time-series sensor signals to detect incipient anomalies that arise during operation of the asset. Finally, when an incipient anomaly is detected, the system generates a notification regarding the anomaly. Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT MICHAEL J SINGLETARY whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)272-4593 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT Monday-Friday 8:00am-5:00pm . Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, FILLIN "SPE Name?" \* MERGEFORMAT Catherine Rastovski can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT 571-270-0349 . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /MICHAEL J SINGLETARY/ Examiner, Art Unit 2857