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 appl icant regards as his invention. Claims 1-13 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. With regards to Claim 1 , the feature “ … continuously or repeatedly recording data including measured values of the measurand and their time of determination, determining filtered values of the measured values by filtering the measured values, based on training data included in the recorded data …” is indefinite as it is unclear what is the “training data” because the preceding language is silent about recorded/recording “training data”. For the purpose of a compact prosecution, the examiner treated this feature as simply “ based on the recorded data …” 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- 1 3 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 non-transitory computer readable medium storing instructions that, when executed by a computer, cause it to perform the following computer implemented outlier detection method comprising the steps of: continuously or repeatedly recording data including measured values of the measurand and their time of determination, determining filtered values of the measured values by filtering the measured values, based on training data included in the recorded data determining a combined distribution of differences between individual measured values and the filtered value of the measured value preceding the respective individual measured value to be expected in the specific application where the outlier detection method is applied by performing the steps of: based on the filtered values of the measured values included in the training data determining a difference distribution of first differences of the filtered values, determining a noise distribution of noise included in the measured values, and based on the noise distribution and the difference distribution determining the combined distribution, identifying outliers by for at least one, several or each new measured value performing the steps of: determining a difference between the respective new measured value and the filtered value of the measured value preceding the respective new measured value, determining a probability of occurrence of this difference between the respective new measured value and the filtered value of the preceding measured value according to the combined distribution , and identifying the respective new measured value as an outlier when the probability of occurrence of this difference is lower than a predetermined level of confidence , and providing a detection result by performing at least one of: indicating each new measured value that has been identified as an outlier, issuing a warning when an outlier has been identified, and issuing a notification or an alarm when a predetermined number of consecutively determined new measured values has been identified as outliers .” 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 groupings of subject matter 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 “ determining filtered values of the measured values by filtering the measured values, based on training data included in the recorded data determining a combined distribution of differences between individual measured values and the filtered value of the measured value preceding the respective individual measured value to be expected in the specific application where the outlier detection method is applied by performing the steps of: based on the filtered values of the measured values included in the training data determining a difference distribution of first differences of the filtered values, determining a noise distribution of noise included in the measured values, and based on the noise distribution and the difference distribution determining the combined distribution, identifying outliers by for at least one, several or each new measured value performing the steps of: determining a difference between the respective new measured value and the filtered value of the measured value preceding the respective new measured value, determining a probability of occurrence of this difference between the respective new measured value and the filtered value of the preceding measured value according to the combined distribution ” are treated as belonging to the mathematical concepts grouping while the steps of “ identifying the respective new measured value as an outlier when the probability of occurrence of this difference is lower than a predetermined level of confidence ” and “ when a predetermined number of consecutively determined new measured values has been identified as outliers” are treated as belonging to mental process grouping (further underlined above) . These mental steps represent a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim element precludes the step s from practically being performed in the mind. For example, “ identifying the respective new measured value as an outlier when the probability of occurrence of this difference is lower than a predetermined level of confidence ” and “ when a predetermined number of consecutively determined new measured values has been identified as outliers ” in the context of this claim these limitations correspondingly encompass the user manually identifying (“judging”) new measured value as an outlier based on (calculated) probability of occurrence of the difference is lower that a (known) predetermined level of confidence and deciding to issue an alarm when the observed number of the determined measured values that has been identified as outliers reach the predetermined number. Similar limitations comprise the abstract ideas of Claim 7 . 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 1: A non-transitory computer readable medium storing instructions that, when executed by a computer, cause it to perform the following computer implemented outlier detection method comprising the steps of: continuously or repeatedly recording data including measured values of the measurand and their time of determination . The additional elements in the preamble (“ A non-transitory computer readable medium storing instructions that, when executed by a computer, cause it to perform the following computer implemented outlier detection method ”) are recited in generality and represent insignificant extra-solution activity (field-of-use limitations) that is not meaningful to indicate a practical application. The additional elements in the preamble such as claims such as a non-transitory computer readable medium storing instructions and a computer are examples of generic computer equipment (components) that are generally recited and, therefore, are not qualified as particular machines. The limitation that generically recite continuously or repeatedly recording data including measured values of the measurand and their time of determination is not meaningful and represent s insignificant extra-solution activity to the judicial exception. According to the October update on 2019 SME Guidance such steps are “performed in order to gather data for the mental analysis step, and is a necessary precursor for all uses of the recited exception. It is thus extra-solution activity, and does 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) because these additional elements/steps are well-understood and conventional in the relevant art based on the prior art of record. The independent claims, therefore, are not patent eligible. With regards to the dependent claims, claims 2 - 11 provide additional features/steps which are part of an expanded abstract idea of the independent claims (additionally comprising mathematical process steps) and, therefore, these claims are not eligible either without additional elements that reflect a practical application and/or qualified for significantly more for substantially similar reasons as discussed with regards to Claim 1 . With regards to Claims 12 and 13, the claims recite additional elements ( computing means connected to and/or communicating with each measurement device and configured to receive the measured values of each measurand, a memory associated to the computing means , Claim 12; the computing means are located in an edge device, in a superordinate unit or in the cloud, and at least one or each measurement device is connected to and/or communicating with the computing means directly, via a superordinate unit, via an edge device located in the vicinity of the respective measurement device, and/or via the internet , Claim 13) that recite generic computer and/or communication equipment/components that are not meaningful and not qualified for a particular machine/practical application/significantly more for substantially similar reasons as discussed with regards to Claim 1. Examiner Note with Regards to Prior Art of Record Claims 1-13 are distinguished over prior art of record based on the reasons below. The following references are considered to be the closest prior art to the claimed invention: Consolatina Liguori et al., “ Outlier Detection for the Evaluation of the Measurement Uncertainty of Environmental Acoustic Noise ”, IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 65, NO. 2, FEBRUARY 2016 , pp. 234-242, addresses the issues of the identification of the outliers occurring in the time history (real time) , following a histogram-based statistical approach, in order to allow the determination of the uncertainty associated with the measurement of the filtered signal . Brian James ( US 6633684 ) discloses recording data including measured values of the measurand and their time of determination and determining filtered values of the measured values by filtering the measured values , determining a noise distribution of noise included in the measured values , performing iterative moving average technique, providing a detection result by performing at least one of: indicating each new measured value that has been identified as an outlier . Tej Radi et al. ( US 11709548 ) discloses detecting/removing outliers by using Kartoffeln filter which is an iterative application . Chu Chen et al. ( CN 114926356 ) discloses a denoising technique that uses filtering dimension strength as weighting. Martin Daumer ( US 6556957 ) discloses a detection an alarm state of measurement signal values received by means for detecting measurement values, wherein an alarm state is detected when the evaluation quantity exceeds an adjustable outlier parameter. Vinod Hegde et al. ( US 2018 / 0189664 ) discloses detect ing outlier events based on sensor data and to issue alerts when such events are detected. Michael J. Leonard et al. ( US 2016 / 0292324 ) discloses removing outliers and using n etwork devices that include devices within the internet of things . Some of these devices may be referred to as edge devices, and may involve edge computing circuitry. Hiroshi Aba ( US 2019 / 0310115 ) discloses extracting data outliers and using measurement operation including an edge computer. However, in regards to Claim 1, the claim differ s from the closest prior art, Liguori , James, Radi , Chen, Daumer , and Hegde, either singularly or in combination, because the references fail to anticipate or render obvious determining a difference distribution of first differences of the filtered values, determining a noise distribution of noise included in the measured values, and based on the noise distribution and the difference distribution determining the combined distribution, identifying outliers by for at least one, several or each new measured value performing the steps of determining a difference between the respective new measured value and the filtered value of the measured value preceding the respective new measured value, determining a probability of occurrence of this difference between the respective new measured value and the filtered value of the preceding measured value according to the combined distribution, and identifying the respective new measured value as an outlier when the probability of occurrence of this difference is lower than a predetermined level of confidence , in combination with all other limitations in the claim as claimed and defined by applicant. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXANDER SATANOVSKY whose telephone number is (571)270-5819 . The examiner can normally be reached on M-F: 9 am-5 pm . If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Catherine Rastovski can be reached on (571) 27 0 - 0349. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public 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, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /ALEXANDER SATANOVSKY/ Primary Examiner, Art Unit 2863