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 § 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.
The claims are evaluated for patent subject matter eligibility under 35 U.S.C. 101 using the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG) as follows:
Step 1:
Claims 1-7 are directed to a method and therefore falls within the four statutory categories of subject matter.
Step 2A:
This step asks if the claim is directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea. Step 2A is a two-prong inquiry: in prong 1 it is determined whether a claim recites a judicial exception, and if so, then in prong 2 it is determined if the recited judicial exception is integrated into a practical application of that exception.
Analyzing claim 1 under prong 1 of step 2A, the abstract idea in bold:
A computer-based method of identifying influential disturbances comprising:
automatically receiving, by a computer, a set of service records from a data source, each service record corresponding to disturbance-revealing events within a specified region occurring during a time period, each service record further including disturbance-related probability values corresponding to the disturbance-revealing events, and wherein one or more service records are mislabeled or have no label relating to an associated disturbance;
automatically removing from the set of service records any service records having a disturbance-related probability value above a threshold that are associated with a known global storm;
automatically generating baselines for a series of relevant subregions associated with a remaining set of service records, and normalizing daily summaries of disturbance probabilities for each of the relevant sub-regions;
automatically identifying subsets of service records corresponding to a series of newly-discovered disturbances by using the disturbance-related probability values and a series of associated features for each of the subset of service records to identify deviations from normal non-disturbance event distributions;
automatically filtering out service records in the identified subset of service records corresponding to know disturbances;
automatically aggregating and splitting the known disturbances and the newly-discovered disturbances to obtain a final set of disturbances, and applying a common metric to determine score for each of the newly-discovered disturbances; and
automatically identifying and outputting a series of influential disturbances, the series of influential disturbances comprising newly-discovered disturbances for which the determined scores are above a predetermined threshold.
has a scope that encompasses mental steps, e.g., concepts that may be performed in the human mind; e.g., human observation/performable with pen and paper/mere data gathering. Claim 1 discloses method of identifying influential disturbances comprising: receiving, a set of service records from a data source, each service record corresponding to disturbance-revealing events within a specified region occurring during a time period, each service record further including disturbance-related probability values corresponding to the disturbance-revealing events, and wherein one or more service records are mislabeled or have no label relating to an associated disturbance; construed by the examiner as a mental step; e.g., mere data gathering; removing from the set of service records any service records having a disturbance-related probability value above a threshold that are associated with a known global storm; construed as a mental step; e.g., observation and/or performable with pen and paper; generating baselines for a series of relevant subregions associated with a remaining set of service records, and normalizing daily summaries of disturbance probabilities for each of the relevant sub-regions; construed by the examiner as a mental step; e.g., performable with pen and paper; identifying subsets of service records corresponding to a series of newly-discovered disturbances by using the disturbance-related probability values and a series of associated features for each of the subset of service records to identify deviations from normal non-disturbance event distributions; construed as a mental step; e.g., observation and/or performable with pen and paper; filtering out service records in the identified subset of service records corresponding to know disturbances; construed as a mental step; e.g., performable with pen and paper; aggregating and splitting the known disturbances and the newly-discovered disturbances to obtain a final set of disturbances, and applying a common metric to determine score for each of the newly-discovered disturbances; and; construed as a mental step; e.g., performable with pen and paper. The broadest reasonable interpretation of the abovementioned steps in light of the specification has a scope that encompasses steps that may be performed in the human mind. It is therefore concluded under prong 1 of step 2A that claim 1 recites a judicial exception in the form of an abstract idea, i.e., mental steps. See MPEP 2106.04(a)(2)(A-C) and MPEP 2106.05(f).
In prong 2 of step 2A it is determined whether the recited judicial exception is integrated into a practical application of that exception by: (1) identifying whether there are any additional elements recited in the claim beyond judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application.
Analyzing claim 1 under prong 2 of step 2A, in addition to the abstract ideas described above, claim 1 further recites:
A computer-based
automatically, by a computer
Analyzing these additional elements of claim 1 under prong 2 of step 2A, these additional elements appear to merely recite the use of a generic processor/computer as a tool to implement the abstract idea and/or to perform functions in its ordinary capacity, e.g., receive, store, or transmit data. However, use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer component after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See MPEP 2106.05(f). Examiner further notes that utilizing a computer in its ordinary capacity also includes utilizing a computer to implement functions automatically, wherein implementing the abstract idea automated from a computer does not integrate a judicial exception into a practical application or provide significantly more.
identifying and outputting a series of influential disturbances, the series of influential disturbances comprising newly-discovered disturbances for which the determined scores are above a predetermined threshold.
Analyzing this additional element of claim 1 under prong 2 of step 2A, this additional element appears to merely collect and interpolate mathematical data, interpreted by the examiner as insignificant extra-solution activity. The term “extra-solution activity” can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps. An example of post-solution activity is an element that is not integrated into the claim as a whole, which is recited in a claim to analyze and manipulate information. See MPEP 2016.05(g). Also, employing well-known computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not integrate the exception into a practical application or add significantly more. See MPEP 2106.07(a).II.
Step 2B:
In step 2B it is determined whether the claim recites additional elements that amount to significantly more than the judicial exception. The additional elements discussed above in connection with prong 2 of step 2A merely represents implementation of the abstract idea using a generic processor/computer and use of a generic processor/computer. However, use of a computer or other machine in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See MPEP 2106.05(f).
The further additional elements discussed above in connection with prong 2 of step 2A also merely represents insignificant extra-solution activity. The term “extra-solution activity” can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps. An example of post solution activity is an element that is not integrated into the claim as a whole, which is recited in a claim to analyze and manipulate information. See MPEP 2016.05(g).
It is therefore concluded under step 2B that claim 1 does not recite additional elements that amount to significantly more than the judicial exception.
Dependent claims 2-7 merely recite further details of the abstract idea of claim 1 and therefore do not represent any additional elements that would integrate the abstract idea into a practical application or represent significantly more than the abstract idea itself.
Step 1:
Claims 8-14 are directed to a system and therefore falls within the four statutory categories of subject matter.
Step 2A:
This step asks if the claim is directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea. Step 2A is a two-prong inquiry: in prong 1 it is determined whether a claim recites a judicial exception, and if so, then in prong 2 it is determined if the recited judicial exception is integrated into a practical application of that exception.
Analyzing claim 8 under prong 1 of step 2A, the abstract idea in bold:
A computer system, the computer system comprising:
one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage medium, and program instructions stored on at least one of the one or more computer-readable tangible storage medium for execution by at least one of the one or more processors via at least one of the one or more computer-readable memories, wherein the computer system is capable of performing a method comprising:
automatically receiving, by a computer, a set of service records from a data source, each service record corresponding to disturbance-revealing events within a specified region occurring during a time period, each service record further including disturbance-related probability values corresponding to the disturbance-revealing events, and wherein one or more service records are mislabeled or have no label relating to an associated disturbance;
automatically removing from the set of service records any service records having a disturbance-related probability value above a threshold that are associated with a known global storm;
automatically generating baselines for a series of relevant subregions associated with a remaining set of service records, and normalizing daily summaries of disturbance probabilities for each of the relevant sub-regions;
automatically identifying subsets of service records corresponding to a series of newly-discovered disturbances by using the disturbance-related probability values and a series of associated features for each of the subset of service records to identify deviations from normal non-disturbance event distributions;
automatically filtering out service records in the identified subset of service records corresponding to known disturbances;
automatically aggregating and splitting the known disturbances and the newly-discovered disturbances to obtain a final set of disturbances, and applying a common metric to determine a score for each of the newly-discovered disturbances; and
automatically identifying and outputting a series of influential disturbances, the series of influential disturbances comprising newly-discovered disturbances for which the determined scores are above a predetermined threshold.
has a scope that encompasses mental steps, e.g., concepts that may be performed in the human mind; e.g., human observation/performable with pen and paper/mere data gathering. Claim 8 discloses receiving, a set of service records from a data source, each service record corresponding to disturbance-revealing events within a specified region occurring during a time period, each service record further including disturbance-related probability values corresponding to the disturbance-revealing events, and wherein one or more service records are mislabeled or have no label relating to an associated disturbance; construed as a mental step; e.g., mere data gathering; removing from the set of service records any service records having a disturbance-related probability value above a threshold that are associated with a known global storm; construed as a mental step; e.g., observation and/or performable with pen and paper; generating baselines for a series of relevant subregions associated with a remaining set of service records, and normalizing daily summaries of disturbance probabilities for each of the relevant sub-regions; construed as a mental step; e.g., performable with pen and paper; identifying subsets of service records corresponding to a series of newly-discovered disturbances by using the disturbance-related probability values and a series of associated features for each of the subset of service records to identify deviations from normal non-disturbance event distributions; construed as a mental step; e.g., observation and/or performable with pen and paper; filtering out service records in the identified subset of service records corresponding to known disturbances; construed as a mental step; e.g., performable with pen and paper; aggregating and splitting the known disturbances and the newly-discovered disturbances to obtain a final set of disturbances, and applying a common metric to determine a score for each of the newly-discovered disturbances; and; construed as a mental step; e.g., performable with pen and paper. The broadest reasonable interpretation of the abovementioned steps in light of the specification has a scope that encompasses steps that may be performed in the human mind. It is therefore concluded under prong 1 of step 2A that claim 8 recites a judicial exception in the form of an abstract idea, i.e., mental steps. See MPEP 2106.04(a)(2)(A-C) and MPEP 2106.05(f).
In prong 2 of step 2A it is determined whether the recited judicial exception is integrated into a practical application of that exception by: (1) identifying whether there are any additional elements recited in the claim beyond judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application.
Analyzing claim 8 under prong 2 of step 2A, in addition to the abstract ideas described above, claim 8 further recites:
A computer system, the computer system
one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage medium, and program instructions stored on at least one of the one or more computer-readable tangible storage medium for execution by at least one of the one or more processors via at least one of the one or more computer-readable memories, wherein the computer system is capable of performing a method
automatically, by a computer
Analyzing these additional elements of claim 8 under prong 2 of step 2A, these additional elements appear to merely recite the use of a generic processor/computer as a tool to implement the abstract idea and/or to perform functions in its ordinary capacity, e.g., receive, store, or transmit data. However, use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer component after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See MPEP 2106.05(f). Examiner further notes that utilizing a computer in its ordinary capacity also includes utilizing a computer to implement functions automatically, wherein implementing the abstract idea automated from a computer does not integrate a judicial exception into a practical application or provide significantly more.
identifying and outputting a series of influential disturbances, the series of influential disturbances comprising newly-discovered disturbances for which the determined scores are above a predetermined threshold.
Analyzing this additional element of claim 8 under prong 2 of step 2A, this additional element appears to merely collect and interpolate mathematical data, interpreted by the examiner as insignificant extra-solution activity. The term “extra-solution activity” can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps. An example of post-solution activity is an element that is not integrated into the claim as a whole, which is recited in a claim to analyze and manipulate information. See MPEP 2016.05(g). Also, employing well-known computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not integrate the exception into a practical application or add significantly more. See MPEP 2106.07(a).II.
Step 2B:
In step 2B it is determined whether the claim recites additional elements that amount to significantly more than the judicial exception. The additional elements discussed above in connection with prong 2 of step 2A merely represents implementation of the abstract idea using a generic processor/computer and use of a generic processor/computer. However, use of a computer or other machine in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See MPEP 2106.05(f).
The further additional elements discussed above in connection with prong 2 of step 2A also merely represents insignificant extra-solution activity. The term “extra-solution activity” can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps. An example of post solution activity is an element that is not integrated into the claim as a whole, which is recited in a claim to analyze and manipulate information. See MPEP 2016.05(g).
It is therefore concluded under step 2B that claim 8 does not recite additional elements that amount to significantly more than the judicial exception.
Dependent claims 9-14 merely recite further details of the abstract idea of claim 8 and therefore do not represent any additional elements that would integrate the abstract idea into a practical application or represent significantly more than the abstract idea itself.
Step 1:
Claims 15-20 are directed to a product and therefore falls within the four statutory categories of subject matter.
Step 2A:
This step asks if the claim is directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea. Step 2A is a two-prong inquiry: in prong 1 it is determined whether a claim recites a judicial exception, and if so, then in prong 2 it is determined if the recited judicial exception is integrated into a practical application of that exception.
Analyzing claim 15 under prong 1 of step 2A, the language:
A computer program product, the computer program product comprising:
one or more computer-readable tangible storage medium and program instructions stored on at least one of the one or more computer-readable tangible storage medium, the program instructions executable by a processor capable of performing a method, the method comprising:
automatically receiving, by a computer, a set of service records from a data source, each service record corresponding to disturbance-revealing events within a specified region occurring during a time period, each service record further including disturbance-related probability values corresponding to the disturbance-revealing events, and wherein one or more service records are mislabeled or have no label relating to an associated disturbance;
automatically removing from the set of service records any service records having a disturbance-related probability value above a threshold that are associated with a known global storm;
automatically generating baselines for a series of relevant subregions associated with a remaining set of service records, and normalizing daily summaries of disturbance probabilities for each of the relevant sub-regions;
automatically identifying subsets of service records corresponding to a series of newly-discovered disturbances by using the disturbance-related probability values and a series of associated features for each of the subset of service records to identify deviations from normal non-disturbance event distributions;
automatically filtering out service records in the identified subset of service records corresponding to known disturbances;
automatically aggregating and splitting the known disturbances and the newly-discovered disturbances to obtain a final set of disturbances, and applying a common metric to determine a score for each of the newly-discovered disturbances; and
automatically identifying and outputting a series of influential disturbances, the series of influential disturbances comprising newly-discovered disturbances for which the determined scores are above a predetermined threshold.
has a scope that encompasses mental steps, e.g., concepts that may be performed in the human mind; e.g., human observation/performable with pen and paper/mere data gathering. Claim 15 discloses the method comprising: receiving, a set of service records from a data source, each service record corresponding to disturbance-revealing events within a specified region occurring during a time period, each service record further including disturbance-related probability values corresponding to the disturbance-revealing events, and wherein one or more service records are mislabeled or have no label relating to an associated disturbance; construed as a mental step; e.g., mere data gathering; removing from the set of service records any service records having a disturbance-related probability value above a threshold that are associated with a known global storm; construed as a mental step; e.g., observation and/or mere data gathering; generating baselines for a series of relevant subregions associated with a remaining set of service records, and normalizing daily summaries of disturbance probabilities for each of the relevant sub-regions; construed as a mental step; e.g., performable with pen and paper; identifying subsets of service records corresponding to a series of newly-discovered disturbances by using the disturbance-related probability values and a series of associated features for each of the subset of service records to identify deviations from normal non-disturbance event distributions; construed as a mental step; e.g., performable with pen and paper; filtering out service records in the identified subset of service records corresponding to known disturbances; construed as a mental step; e.g., observation and/or performable with pen and paper; aggregating and splitting the known disturbances and the newly-discovered disturbances to obtain a final set of disturbances, and applying a common metric to determine a score for each of the newly-discovered disturbances; and; construed as a mental step; e.g., performable with pen and paper. The broadest reasonable interpretation of the abovementioned steps in light of the specification has a scope that encompasses steps that may be performed in the human mind. It is therefore concluded under prong 1 of step 2A that claim 15 recites a judicial exception in the form of an abstract idea, i.e., mental steps. See MPEP 2106.04(a)(2)(A-C) and MPEP 2106.05(f).
In prong 2 of step 2A it is determined whether the recited judicial exception is integrated into a practical application of that exception by: (1) identifying whether there are any additional elements recited in the claim beyond judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application.
Analyzing claim 15 under prong 2 of step 2A, in addition to the abstract ideas described above, claim 15 further recites:
A computer program product, the computer program product comprising:
one or more computer-readable tangible storage medium and program instructions stored on at least one of the one or more computer-readable tangible storage medium, the program instructions executable by a processor capable of performing a method,
automatically, by a computer
Analyzing these additional elements of claim 15 under prong 2 of step 2A, these additional elements appear to merely recite the use of a generic processor/computer as a tool to implement the abstract idea and/or to perform functions in its ordinary capacity, e.g., receive, store, or transmit data. However, use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer component after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See MPEP 2106.05(f)]. Examiner further notes that utilizing a computer in its ordinary capacity also includes utilizing a computer to implement functions automatically, wherein implementing the abstract idea automated from a computer does not integrate a judicial exception into a practical application or provide significantly more.
identifying and outputting a series of influential disturbances, the series of influential disturbances comprising newly-discovered disturbances for which the determined scores are above a predetermined threshold.
Analyzing this additional element of claim 15 under prong 2 of step 2A, this additional element appears to merely collect and interpolate mathematical data, interpreted by the examiner as insignificant extra-solution activity. The term “extra-solution activity” can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps. An example of post-solution activity is an element that is not integrated into the claim as a whole, which is recited in a claim to analyze and manipulate information. See MPEP 2016.05(g). Also, employing well-known computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not integrate the exception into a practical application or add significantly more. See MPEP 2106.07(a).II.
Step 2B:
In step 2B it is determined whether the claim recites additional elements that amount to significantly more than the judicial exception. The additional elements discussed above in connection with prong 2 of step 2A merely represents implementation of the abstract idea using a generic processor/computer and use of a generic processor/computer. However, use of a computer or other machine in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See MPEP 2106.05(f).
The further additional elements discussed above in connection with prong 2 of step 2A also merely represents insignificant extra-solution activity. The term “extra-solution activity” can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps. An example of post solution activity is an element that is not integrated into the claim as a whole, which is recited in a claim to analyze and manipulate information. See MPEP 2016.05(g).
It is therefore concluded under step 2B that claim 15 does not recite additional elements that amount to significantly more than the judicial exception.
Dependent claims 16-20 merely recite further details of the abstract idea of claim 15 and therefore do not represent any additional elements that would integrate the abstract idea into a practical application or represent significantly more than the abstract idea itself.
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 (i.e., changing from AIA to pre-AIA ) 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, 3, 8, 10, 15, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Scolnicov et al. (US 2013/0332090 A1), hereinafter Scolnicov, in view of Vedula (US 2015/0213454 A1), hereinafter Vedula.
Regarding claim 1, Scolnicov discloses A computer-based method of identifying influential disturbances comprising:
receiving, by a computer, (Scolnicov, e.g., see fig. 1 illustrating composite event detection and classification system (100), anomaly detection system (106), event database (108), and event detection systems (110); see also para. [0031] disclosing that (100), (106), and (110) are composed of software systems residing and operating on computer hardware devices, and that elements (100)-(116) may be contained in or reside on the same computerized device).
a set of service records from a data source, each service record corresponding to disturbance-revealing events within a specified region occurring during a time period, (Scolnicov, e.g., see rejection as applied above, specifically with regard to fig. 1; see also fig. 2 illustrating step (201), receive data from one or more sources; construed by the examiner as “service records;” see also para. [0060] disclosing the generation of candidate events; construed as a disturbance-revealing event, wherein the sources may be from sensors, anomaly detection systems, event databases and event detection systems, and received by the composite event detection and classification system, wherein the data is identified as a pool of candidate events; see also fig. 4 illustrating District Metered Areas (DMAs) (410)-(411); see also para. [0071] disclosing that the meters may be grouped geographically by zone or by DMAs; construed as a specified region, wherein data from water network (300) or water network (301) reports data from specific meters or collection of meters; see also paras. [0036]-[0037] disclosing sensors (104) include sensor (S1), (S2), and (S3); construed as a data source, which send time-dependent data representative of operational parameters of the network, such as water flow, pressure, turbidity, reservoir level, chlorine level, and pH level, as raw data to anomaly detection system (106). Based on the raw data, anomaly detection system (106) reports candidate events to composite event detection and classification system (100). The present invention allows for data to be received as candidate events (102) directly from sensors in the network, anomaly detection system (106), event database (108), or event detection systems (110)).
each service record further including disturbance-related probability values corresponding to the disturbance-revealing events, and wherein one or more service records are mislabeled or have no label relating to an associated disturbance; (Scolnicov, e.g., see rejection as applied above; see also para. [0039] disclosing anomaly detectors (106) include anomaly detectors for testing the likelihood of no anomaly for sensors and for testing the likelihood of alternative hypothesis such as specific event types. Anomaly detectors (106) send anomalies as candidate events (102) to composite event detection and classification system (100). For each data set, each anomaly detector determines, by analyzing the significance of deviations, the statistical likelihood that no relevant anomaly occurred given the sensor readings during a given time period; e.g., minutes, hours, days or longer; see also para. [0042] disclosing a probability of an anomalous event given a value of, e.g., 10% probability, 1% probability, etc.; see also fig. 1 illustrating composite event type classifiers 1, 2, …N of composite event detection and classification system (100); see also para. [0043] disclosing each candidate event or a combination of two or more events may be classified into events or anomalies, based on a fingerprint or signature characterizing the events and anomalies, wherein the signature or fingerprint may be apparent only when two or more candidate events are taken together; examiner notes that a candidate event is given a signature/fingerprint; construed as a label, only after merging at least two candidate events derived from the sensor data, which is necessarily describing not having a label relating to an associated disturbance/anomaly).
removing from the set of service records any service records having a disturbance-related probability value above a threshold that are associated with a known global storm; (Scolnicov, e.g., see rejection as applied above; see also fig. 6; see also paras. [0088]-[0090] disclosing anomaly detectors which are set to a relatively low statistical threshold for examining events occurring over relatively long periods of time. A p-value is selected based on the given combination rule; see also paras. [0091]-[0093] disclosing analysis of the significance of deviations of sensor readings as compared to historical statistical data. The statistical deviation is measured by the historically observed distribution of deviations as a function of parameters. One such parameter may include weather measurements such as temperature, humidity, or weather warnings. A determination is made of whether output from the anomaly detector is below the selected p-value threshold, step (615). If the output is not below the p-value threshold, the system returns and examines another sensor data, step (609). However, if the output of the anomaly detector is below the p-value threshold, the process proceeds to step (617) where the given sensor data is recorded as a candidate event. The recorded candidate event may be added to a list or pool of candidate events; examiner notes that output not below the p-value (construed as above) is discarded which is construed as removing, and wherein a weather warning is construed as an association with a global storm as it is a storm that is not unknown or hidden).
generating baselines for a series of relevant subregions associated with a remaining set of service records, and normalizing daily summaries of disturbance probabilities for each of the relevant sub-regions; (Scolnicov, e.g., see rejection as applied above; see also fig. 5 illustrating DMA (505), which is divided into sub-regions by a valve (504), wherein sensor (503) is of a region of DMA (505) separated from the sub-region of DMA (505) comprising sensors (501) and (502); see also para. [0082] disclosing sensor (501), (502), (503), and valve (504) are illustrated as being within the boundaries of DMA (505) represents a sub-network of pipes or sensors grouped within a given geographical region. Water main (500) serves as a primary supply pipe and may be tapped by secondary water pipes, such as the pipes leading to sensors (501) and (503). These secondary water pipes form subnetworks which may be characterized as DMAs and provide for monitored water distribution to regions associated with the DMAs. The data collected by sensors (501)-(503) may be used to identify anomalies; see also para. [0091] disclosing the sensor data used may be a processed version of the original sensor data received, and may be further restricted in time from the entire historical data. For example, the data sets used for the above analysis may be the average sensor values calculated over consecutive 6-hour periods (one average value for each sensor for every 6 hours). Analyzing the significant deviations, which may be significant in light of the historical statistical data. For each data set, each anomaly detector determines, by analyzing the significance of deviations, the statistical likelihood that no relevant anomaly occurred given the sensor readings during a given time period. Anomaly detectors analyze the significance of deviations over time, e.g., over minutes, hours, days or longer, since, for example, the continued or frequent occurrence of the deviations raise the significance of such deviations. The system considers normalized values that best describe the anomaly type or types as detected by the anomaly detection methods of the anomaly detectors; examiner notes a probability or disturbance is generated from anomalies, which are explicitly disclosed as taken (among other time metrics) daily, which is disclosed in para. [0042]; see also fig. 7).
identifying subsets of service records corresponding to a series of newly-discovered disturbances by using the disturbance-related probability values and a series of associated features for each of the subset of service records to identify deviations from normal non-disturbance event distributions; (Scolnicov, e.g., see rejection as applied above; see also figs. 6 and 8-9 and paras. [0105]-[0106] disclosing in step (901), the system selects time intervals before and after a start time for a candidate event in a set of candidate events determined from, in one embodiment, the steps described in fig. 6. The start time of the candidate event may be a likely start time determined by the method described with respect to fig. 8. The system compares a top X% of data points in the candidate event before the time interval to a bottom Y% after the time interval in step (903). It does this if the candidate event type is a “decrease,” and it does the opposite (compares the bottom X% of data points in the candidate event before the time interval to the top Y% after the time interval) if it is an “increase.” It is determined whether the entire top X% of data points are greater than all the data points of the bottom Y%, step (905). This ensures that the values shortly before the start time were (with some limited 100-X% exceptions) greater (or smaller) than the values shortly after it (with some limited 100-Y% exceptions; examiner notes that data immediately preceding and immediately succeeding the candidate event provided by the sensor data are construed as a subset of service records corresponding to a series of newly-discovered disturbances, which explicitly utilizes percentages of likelihood; e.g., see para. [0090]. Also, see para. [0091] disclosing the sensor data used may be a processed version of the original sensor data received, and may be further restricted in time from the entire historical data. For example, the data sets used for the above analysis may be the average sensor values calculated over consecutive 6-hour periods (one average value for each sensor for every 6 hours). Analyzing the significance of deviations, for example, a sensor reading, when compared to the historical statistical data, may be significant in light of the historical statistical data. For each data set, each anomaly detected determines, by analyzing the significance of deviations, the statistical likelihood that no relevant anomaly occurred given the sensor readings during a given time period; examiner notes deviations from no relevant anomaly occurr[ing] is construed as deviations from normal non-disturbance event distributions).
filtering out service records in the identified subset of service records corresponding to known disturbances; (Scolnicov, e.g., see rejection as applied above; see also para. [0074] disclosing data preparation engine (304) extracts the data elements from the network data and formats them into a consistent format. Among filtered information may be noise associated with the data transmission from aspects of the resources, such as for example noisy data transmission from a meter, or errors associated with the data measurements, transmissions or collection).
aggregating and splitting the known disturbances and the newly-discovered disturbances to obtain a final set of disturbances, and applying a common metric to determine a score for each of the newly-discovered disturbances; and (Scolnicov, e.g., see rejection as applied above, specifically disclosing known and new-discovered disturbances as candidate events; see also fig. 8 and paras. [0099]-[0104] disclosing steps (803)-(819), specifically one or more interval parameters are retrieved from the combination rule, step (803). A sample size is determined based on a frequency of data points in the candidate events, step (805); construed as aggregating. In a next step (807), a fixed interval is selected based on the sample size and interval parameters; construed as splitting. All of the start times are iterated through, step (809). For a given start time, a modified “T-test” is run over the fixed interval, step (811). The test iterates through all of times t within the suspect duration. This “best separation” time is found by testing the pairs of intervals around all times t. A score is computed according to the modified T-test, step (813). The score is calculated for the change between interval1 (ending at t) and interval2 (starting at t); wherein t is construed as a common metric. The system determines whether all the intervals have been run, step (815). When all the intervals have been run, a highest score is selected in step (817) to determine the most likely start time for the candidate events. This start time with the highest score is selected, step (817) and the system proceeds to a next set of candidate events to determine a likely start time, step (821); construed as candidate events representative of the known and newly discovered disturbances; wherein the examiner notes that flowchart of fig. 8 returns from step (819) to step (821) iteratively).
identifying and outputting a series of influential disturbances, the series of influential disturbances comprising newly-discovered disturbances for which the determined scores are above a predetermined threshold. (Scolnicov, e.g., see rejection as applied above; see also fig. 12 and paras. [0115]-[0117] disclosing steps (1201)-(1215), specifically anomaly magnitudes for each candidate event in a set are retrieved form event information, step (1201). In a next step (1203); disclosed in fig. 12 as “Magnitude values of candidate events within threshold margin of error; examiner notes that a threshold margin of error comprises the broadest reasonable interpretation as both below and above, which meets the limitation of above a predetermined threshold; according to the combination rule “ is determined as an affirmative or negative; and states a determination is made whether magnitude values of the candidate events are known with sufficient accuracy, according to the given combination rule. If the candidates are within the margin of error, an anomaly magnitude relationship is determined between the candidate events in the set, step (1205). If the magnitude of the anomaly of the component events may be reliably measured, the system selects only sets of candidate events with matching magnitudes, as determined by the event type being searched for. Parameters are retrieved, which are determined by the combination rule, step (1207). In a next step (1209), a determination is made whether the events have corresponding anomaly magnitudes based on the retrieved parameters. That candidate events are determined to either have corresponding anomaly magnitudes or not. If the candidate events have corresponding magnitudes, the set of candidate events is identified as having matching anomaly magnitudes; construed as a series of influential disturbances comprising newly discovered disturbances, step (1211)).
Scolnicov is not relied upon as explicitly disclosing that the various functions of the method are performed: automatically.
However, Vedula further discloses automatically (Vedula, e.g., see para. [0055] disclosing each of the methods described herein may be implemented by a computer system. Each step of these methods may be executed automatically by the computer system, and/or may be provided with inputs/outputs involving a user).
Accordingly, it would be prima facie obvious to one of ordinary skill in the art, at the time the invention was effectively filed, to have modified Scolnicov with Vedula’s automatically for at least the reasons that it is known to automate instructions performed on a computer utilizing program instructions stored on a non-transitory medium; e.g., see Vedula, para. [0055].
Regarding claim 3, Scolnicov in view of Vedula discloses: The computer-based method of claim 1, the method further comprising:
automatically determining start dates and end dates for each of the identified subsets of service records corresponding to the series of newly-discovered disturbances. (see rejection as applied to claim 1, specifically to Scolnicov, e.g., see para. [0039] disclosing some of those anomalies represent events in and of themselves, and some represent parts of events such as the start of an event, the end of an event, substantial change in an event, peak of an event, and the like. For each data set, each anomaly detector determines, by analyzing the significance of deviations, the statistical likelihood that no relevant anomaly occurred given the sensor readings during a given time period; see also paras. [0042] and [0061]).
Regarding claim 8, Claim 8 recites A computer system, the computer system comprising: one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage medium, and program instructions stored on at least one of the one or more computer-readable tangible storage medium for execution by at least one of the one or more processors via at least one of the one or more computer-readable memories, wherein the computer system is capable of performing a method comprising: automatically receiving, by a computer, a set of service records from a data source, each service record corresponding to disturbance-revealing events within a specified region occurring during a time period, each service record further including disturbance-related probability values corresponding to the disturbance-revealing events, and wherein one or more service records are mislabeled or have no label relating to an associated disturbance; automatically removing from the set of service records any service records having a disturbance-related probability value above a threshold that are associated with a known global storm; automatically generating baselines for a series of relevant subregions associated with a remaining set of service records, and normalizing daily summaries of disturbance probabilities for each of the relevant sub-regions; automatically identifying subsets of service records corresponding to a series of newly-discovered disturbances by using the disturbance-related probability values and a series of associated features for each of the subset of service records to identify deviations from normal non-disturbance event distributions; automatically filtering out service records in the identified subset of service records corresponding to known disturbances; automatically aggregating and splitting the known disturbances and the newly-discovered disturbances to obtain a final set of disturbances, and applying a common metric to determine a score for each of the newly-discovered disturbances; and automatically identifying and outputting a series of influential disturbances, the series of influential disturbances comprising newly-discovered disturbances for which the determined scores are above a predetermined threshold., and is rejected under 35 U.S.C. 103 as being unpatentable by Scolnicov in view of Vedula for reasons analogous to those set forth in connection with claim 1.
Regarding claim 10, claim 10 discloses The computer system of claim 8, the method further comprising: automatically determining start dates and end dates for each of the identified subsets of service records corresponding to the series of newly-discovered disturbances., and is rejected under 35 U.S.C. 103 as being unpatentable by Scolnicov in view of Vedula for reasons analogous to those set forth in connection with claim 3.
Regarding claim 15, Claim 15 recites A computer program product, the computer program product comprising: one or more computer-readable tangible storage medium and program instructions stored on at least one of the one or more computer-readable tangible storage medium, the program instructions executable by a processor capable of performing a method, the method comprising: automatically receiving, by a computer, a set of service records from a data source, each service record corresponding to disturbance-revealing events within a specified region occurring during a time period, each service record further including disturbance-related probability values corresponding to the disturbance-revealing events, and wherein one or more service records are mislabeled or have no label relating to an associated disturbance; automatically removing from the set of service records any service records having a disturbance-related probability value above a threshold that are associated with a known global storm; automatically generating baselines for a series of relevant subregions associated with a remaining set of service records, and normalizing daily summaries of disturbance probabilities for each of the relevant sub-regions; automatically identifying subsets of service records corresponding to a series of newly-discovered disturbances by using the disturbance-related probability values and a series of associated features for each of the subset of service records to identify deviations from normal non-disturbance event distributions; automatically filtering out service records in the identified subset of service records corresponding to known disturbances; automatically aggregating and splitting the known disturbances and the newly-discovered disturbances to obtain a final set of disturbances, and applying a common metric to determine a score for each of the newly-discovered disturbances; and automatically identifying and outputting a series of influential disturbances, the series of influential disturbances comprising newly-discovered disturbances for which the determined scores are above a predetermined threshold., and is rejected under 35 U.S.C. 103 as being unpatentable by Scolnicov in view Vedula for reasons analogous to those set forth in connection with claim 1.
Regarding claim 17, claim 17 discloses The computer program product of claim 15, the method further comprising: automatically determining start dates and end dates for each of the identified subsets of service records corresponding to the series of newly-discovered disturbances., and is rejected under 35 U.S.C. 103 as being unpatentable by Scolnicov in view of Vedula for reasons analogous to those set forth in connection with claim 3.
Claims 2, 9, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Scolnicov in view of Vedula, in further view of Wu et al. (US 11,960,254 B1), hereinafter Wu.
Regarding claim 2, Scolnicov in view of Vedula discloses: The computer-based method of claim 1, wherein automatically identifying the subsets of the service records corresponding to the series of the newly-discovered disturbances by using the disturbance-related probability values and the series of associated features for each of the subset of service records to identify the deviations from the normal non-disturbance event distributions further comprises:
automatically utilizing an algorithm to identify disturbance signals. (Scolnicov, e.g., see rejection as applied to claim 1 of Scolnicov in view of Vedula disclosing fig. 8; see also para. [0100] disclosing in step (801), the system retrieves a combination rule for a candidate event. One or more interval parameters are retrieved from the combination rule, step (803). A combination rule may include one or more algorithms or parameters for a specific event type; see also para. [0104] disclosing the method used to determine start time, for example the method described in the preceding paragraphs, may be such that it always returns a result, the time most likely to be an event star time, if indeed the candidate event describes a real-world anomaly).
Scolnicov, of Scolnicov in view of Vedula, discloses candidate events may have one or more pre-defined p-value threshold values, time windows, and a skip value (interval) for how much to move or shift the time windows for testing against a threshold, depending on the event type of the candidate events; e.g., see para. [0089], but is not relied upon as explicitly disclosing a calibrated cumulative sum (CUSUM) algorithm.
However, Wu further discloses a calibrated cumulative sum (CUSUM) algorithm. (Wu, e.g., see col. 7, lines 24-39 disclosing a CUSUM algorithm is an algorithm used for monitoring small shifts in data. it involves the calculation of a cumulative sum and comparison of the cumulative sum to a threshold value. When the value of the cumulative sum exceeds the threshold value, a shift is identified. A CUSUM algorithm may calculate a high side cumulative sum (HS) and a low side cumulative sum (SL) of data).
Accordingly, it would be prima facie obvious to one of ordinary skill in the art, at the time the invention was effectively filed, to have modified Scolnicov in view of Vedula’s method with Wu’s calibrated cumulative sum (CUSUM) algorithm for at least the reasons that a CUSUM integrates small deviations/shifts over time to accumulate a detectable signal, wherein CUSUM detects the mean of a time-series that is stationary between two changepoints for turning subtle mean shifts into large, detectable signals.
Regarding claim 9, claim 9 discloses The computer system of claim 8, wherein automatically identifying the subsets of the service records corresponding to the series of the newly-discovered disturbances by using the disturbance-related probability values and the series of associated features for each of the subset of service records to identify the deviations from the normal non-disturbance event distributions further comprises: automatically utilizing a calibrated cumulative sum (CUSUM) algorithm to identify disturbance signals., and is rejected under 35 U.S.C. 103 as being unpatentable by Scolnicov in view of Vedula, in further view of Wu for reasons analogous to those set forth in connection with claim 2.
Regarding claim 16, claim 16 discloses The computer program product of claim 15, wherein automatically identifying the subsets of the service records corresponding to the series of the newly-discovered disturbances by using the disturbance-related probability values and the series of associated features for each of the subset of service records to identify the deviations from the normal non-disturbance event distributions further comprises: automatically utilizing a calibrated cumulative sum (CUSUM) algorithm to identify disturbance signals., and is rejected under 35 U.S.C. 103 as being unpatentable by Scolnicov in view of Vedula, in further view of Wu for reasons analogous to those set forth in connection with claim 2.
Conclusion
Claims 4-7, 11-14, and 18-20 do not stand rejected on the ground(s) of prior art.
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
US 2023/0106360 A1 to Pattipati et al. relates to a physics-informed partial least squares regression modeling for failure detection in power electronic devices.
US 2022/0291420 A1 to Rothenberg relates to a forecasting method with machine learning.
US 2021/0089383 A1 to Kitahara et al. relates to tracking cluster image mutation events.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIC S. VON WALD whose telephone number is (571)272-7116. The examiner can normally be reached Monday - Friday 7:30 - 5:30.
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, Catherine Rastovski can be reached at (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.
/E.S.V./Examiner, Art Unit 2863
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857