DETAILED ACTION
1. Claims 1-20 have been presented for examination.
Notice of Pre-AIA or AIA Status
2. 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.
3. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
i) In view of Step 1 of the analysis, claim(s) 1 is directed to a statutory category as a machine, claim 10 is directed to a statutory category as a process, and claim 16 is directed to an article of manufacture as a non-transitory computer readable medium, which each represent a statutory category of invention. Therefore, claims 1-20 are directed to patent eligible categories of invention.
ii) In view of Step 2A, Prong One, claims 1, 10 and 16 recite the abstract idea of calculating data values based on historical data which constitutes an abstract idea based on Mental Processes based on concepts performed in the human mind, or with the aid of pencil and paper as well as and alternatively as Mathematical Concepts including mathematical formulas or equations as well as calculations.
As per claim 1, the limitation of “generate one or more statistical summaries for the data element based on the historical values for the data element;” would be analogous to a person evaluating historical data and determining a statistical summary of the data and thus fall under Mental Processes. In addition, the steps would constitute Mathematical Concepts including mathematical formulas or equations as well as calculations.
As per claim 1, the limitation of “generate, using a statistical model, a confidence interval defined by an upper threshold and a lower threshold based on the one or more statistical summaries, wherein the upper threshold and the lower threshold define a predicted range for a current value for the data element;” would be analogous to a person evaluating historical data and determining a statistical summary of the data and thus fall under Mental Processes. In addition, the steps would constitute Mathematical Concepts including mathematical formulas or equations as well as calculations.
As per claim 10, the limitation of “generating, by the data quality system, one or more statistical summaries for the data element based on the historical values for the data element;” would be analogous to a person evaluating historical data and determining a statistical summary of the data and thus fall under Mental Processes. In addition, the steps would constitute Mathematical Concepts including mathematical formulas or equations as well as calculations.
As per claim 10, the limitation of “generating, by the data quality system, using an auto-regressive integrated moving average (ARIMA) model, a confidence interval defined by an upper threshold and a lower threshold based on the one or more statistical summaries, wherein the upper threshold and the lower threshold define a predicted range for a current value for the data element, and wherein the ARIMA model applies weights to the historical values for the data element that are progressively heavier for more recent historical values;” would be analogous to a person evaluating the data using statistical calculations of the data and thus fall under Mental Processes. In addition, the steps would constitute Mathematical Concepts including mathematical formulas or equations as well as calculations.
As per claim 10, the limitation of “and generating, by the data quality system, an output that indicates whether the current value for the data element is within the predicted range defined by the upper threshold and the lower threshold;” would be analogous to a person determining the result of the data using statistical calculations of the data and thus fall under Mental Processes. In addition, the steps would constitute Mathematical Concepts including mathematical formulas or equations as well as calculations.
As per claim 16, the limitation of “generate one or more statistical summaries for the data element based on the historical values for the data element;” would be analogous to a person evaluating historical data and determining a statistical summary of the data and thus fall under Mental Processes. In addition, the steps would constitute Mathematical Concepts including mathematical formulas or equations as well as calculations.
As per claim 16, the limitation of “generate, using a statistical model, a confidence interval defined by an upper threshold and a lower threshold based on the one or more statistical summaries, wherein the upper threshold and the lower threshold define a predicted range for a current value for the data element;” would be analogous to a person evaluating the data using statistical calculations of the data and thus fall under Mental Processes. In addition, the steps would constitute Mathematical Concepts including mathematical formulas or equations as well as calculations.
As per claim 16, the limitation of “and generate an output that indicates whether the current value for the data element is within the predicted range defined by the upper threshold and the lower threshold.” would be analogous to a person determining the result of the data using statistical calculations of the data and thus fall under Mental Processes. In addition, the steps would constitute Mathematical Concepts including mathematical formulas or equations as well as calculations.
Thus, the claims recite the abstract idea of a mental process performed in the human mind, or with the aid of pencil and paper, as well as and alternatively as Mathematical Concepts including mathematical formulas or equations as well as calculations.
Further, as to claims 1 and 16, other than reciting “using a processor,” nothing in the claim element precludes the step from practically being performed in the mind.
Dependent claims 2-9, 11-15 and 17-20 further narrow the abstract ideas, identified in the independent claims.
Dependent claims 2-9, 11-15 and 17-20 also constitute an abstract idea based on Mathematical Concepts including mathematical formulas or equations as well as calculations.
iii) In view of Step 2A, Prong Two, the judicial exception is not integrated into a practical application. In Claims 1 and 16, the additional element of “a processor”, and the “non-transitory computer readable medium” in claim 16, merely uses a computer device as a tool to perform the abstract idea. (MPEP 2106.05(f)) The limitation in claim 1, "obtain a historical dataset that includes historical values for a data element; receive a current dataset that includes the current value for the data element; and generate an output that indicates whether the current value for the data element is within the predicted range defined by the upper threshold and the lower threshold”, as well as the limitation in claim 10, “obtaining, by a data quality system, a historical dataset that includes historical values for a data element; receiving, by the data quality system, a current dataset that includes the current value for the data element;”, as well as the limitations in claim 16 “obtain, from a data repository that is updated at periodic intervals, a historical dataset that includes historical values for a data element, wherein the historical values for the data element are stored as structured data in the data repository; receive, based on an update to the structured data in the data repository, a current dataset that includes the current value for the data element;” are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(f)) 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 components after the fact to an abstract idea (e.g., a mental process) does not integrate a judicial exception into a practical application. (MPEP 2106.05(f)(2)) Additionally the limitation in claim 1, "obtain a historical dataset that includes historical values for a data element; receive a current dataset that includes the current value for the data element; and generate an output that indicates whether the current value for the data element is within the predicted range defined by the upper threshold and the lower threshold”, as well as the limitation in claim 10, “obtaining, by a data quality system, a historical dataset that includes historical values for a data element; receiving, by the data quality system, a current dataset that includes the current value for the data element;”, as well as the limitations in claim 16 “obtain, from a data repository that is updated at periodic intervals, a historical dataset that includes historical values for a data element, wherein the historical values for the data element are stored as structured data in the data repository; receive, based on an update to the structured data in the data repository, a current dataset that includes the current value for the data element;” alternatively can be viewed as insignificant extra-solution activity, specifically pertaining to mere data gathering/output necessary to perform the abstract idea (MPEP 2106.05(g)) and is not sufficient to integrate the judicial exception into a practical application. This is akin to selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, which has been identified as extra solution activity. Therefore, the judicial exception is not integrated into a practical application.
Dependent claims 2-9, 11-15 and 17-20 further narrow the abstract ideas, identified in the independent claims and do not introduce further additional elements for consideration beyond those addressed above.
iv) In view of Step 2B, claims 1, 10 and 16 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claims 1 and 16, the additional element of “a processor”, and the “non-transitory computer readable medium”, in claim 16, merely uses a computer device as a tool to perform the abstract idea. (MPEP 2106.05(f)) The limitation in claim 1, "obtain a historical dataset that includes historical values for a data element; receive a current dataset that includes the current value for the data element; and generate an output that indicates whether the current value for the data element is within the predicted range defined by the upper threshold and the lower threshold”, as well as the limitation in claim 10, “obtaining, by a data quality system, a historical dataset that includes historical values for a data element; receiving, by the data quality system, a current dataset that includes the current value for the data element;”, as well as the limitations in claim 16 “obtain, from a data repository that is updated at periodic intervals, a historical dataset that includes historical values for a data element, wherein the historical values for the data element are stored as structured data in the data repository; receive, based on an update to the structured data in the data repository, a current dataset that includes the current value for the data element;” are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(f)) 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 components after the fact to an abstract idea (e.g., a mental process) does not integrate a judicial exception into a practical application. (MPEP 2106.05(f)(2)) Additionally the limitation in claim 1, "obtain a historical dataset that includes historical values for a data element; receive a current dataset that includes the current value for the data element; and generate an output that indicates whether the current value for the data element is within the predicted range defined by the upper threshold and the lower threshold”, as well as the limitation in claim 10, “obtaining, by a data quality system, a historical dataset that includes historical values for a data element; receiving, by the data quality system, a current dataset that includes the current value for the data element;”, as well as the limitations in claim 16 “obtain, from a data repository that is updated at periodic intervals, a historical dataset that includes historical values for a data element, wherein the historical values for the data element are stored as structured data in the data repository; receive, based on an update to the structured data in the data repository, a current dataset that includes the current value for the data element;” alternatively can be viewed as an insignificant extra-solution activity, specifically pertaining to mere data gathering/output necessary to perform the abstract idea (MPEP 2106.05(g)) and is not sufficient to integrate the judicial exception into a practical application. This is akin to selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, which has been identified as extra solution activity. Therefore, the claim as a whole does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, when considered alone or in combination, do not amount to significantly more than the judicial exception. As stated in Section I.B. of the December 16, 2014 101 Examination Guidelines, “[t]o be patent-eligible, a claim that is directed to a judicial exception must include additional features to ensure that the claim describes a process or product that applies the exception in a meaningful way, such that it is more than a drafting effort designed to monopolize the exception.”
The dependent claims include the same abstract ideas recited as recited in the independent claims, and merely incorporate additional details that narrow the abstract ideas and fail to add significantly more to the claims.
Dependent claim 2 further defines the type of statistical calculation which merely narrows the abstract idea identified as a mental process and/or method of organized human activity and represents mathematical concepts including mathematical formulas or equations as well as calculations.
Dependent claims 3 further defines aspects of the statistical calculation which merely narrows the abstract idea identified as a mental process and/or method of organized human activity and represents mathematical concepts including mathematical formulas or equations as well as calculations.
Dependent claim 4, 11, and 17 further defines additional steps of the statistical calculation which merely narrows the abstract idea identified as a mental process and/or method of organized human activity and represents mathematical concepts including mathematical formulas or equations as well as calculations.
Dependent claim 5, 12, and 18 further defines additional steps of the statistical calculation which merely narrows the abstract idea identified as a mental process and/or method of organized human activity and represents mathematical concepts including mathematical formulas or equations as well as calculations.
Dependent claim 6 and 13 further defines additional steps of the statistical calculation which merely narrows the abstract idea identified as a mental process and/or method of organized human activity and represents mathematical concepts including mathematical formulas or equations as well as calculations.
Dependent claim 7, 14, and 19 further defines an output to a client which are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(f)) 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 components after the fact to an abstract idea (e.g., a mental process) does not integrate a judicial exception into a practical application. (MPEP 2106.05(f)(2)) The output can alternatively can be viewed as an insignificant extra-solution activity, specifically pertaining to mere data gathering/output necessary to perform the abstract idea (MPEP 2106.05(g)) and is not sufficient to integrate the judicial exception into a practical application.
Dependent claim 8, 15, and 20 further defines an output to a client which are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(f)) 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 components after the fact to an abstract idea (e.g., a mental process) does not integrate a judicial exception into a practical application. (MPEP 2106.05(f)(2)) The output can alternatively can be viewed as an insignificant extra-solution activity, specifically pertaining to mere data gathering/output necessary to perform the abstract idea (MPEP 2106.05(g)) and is not sufficient to integrate the judicial exception into a practical application.
Dependent claim 9 further defines the storage of data which are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(f)) 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 components after the fact to an abstract idea (e.g., a mental process) does not integrate a judicial exception into a practical application. (MPEP 2106.05(f)(2)) The data storage can alternatively can be viewed as an insignificant extra-solution activity, specifically pertaining to mere data gathering/output necessary to perform the abstract idea (MPEP 2106.05(g)) and is not sufficient to integrate the judicial exception into a practical application.
v) Accordingly, claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without anything significantly more.
Appropriate correction is required.
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) 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.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
4. Claim(s) 1-2, 4-9, and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No. 20120278051, hereafter Jiang in view of U.S. Patent Publication No. 20210344695, hereafter Palani.
Regarding Claim 1: The reference discloses A system for automated data quality monitoring and data governance, comprising: one or more memories; and one or more processors, communicatively coupled to the one or more memories, configured to: obtain a historical dataset that includes historical values for a data element; (Jiang [0003] A method and system for performing analysis of energy consumption in one or more buildings may be provided. The analysis may include anomaly detection, forecasting or root cause analysis of energy consumption, or combinations thereof, for a portfolio of buildings using multi-step statistical modeling. The method, in one aspect, may include receiving energy consumption data associated with a building, receiving building characteristic data associated with the building, receiving building operation and activities data associated with the building, and receiving weather data.) generate one or more statistical summaries for the data element based on the historical values for the data element; (Jiang [0003] “The method may also include fitting said energy consumption data, said building characteristic data, said building operation and activities data, and said weather data to generate a variable based degree model.”) generate, using a statistical model, a confidence interval defined by an upper threshold and a lower threshold based on the one or more statistical summaries, wherein the upper threshold and the lower threshold define a predicted range for a current value (See secondary reference) for the data element; (Jiang [0032] “The methodology of the present disclosure detects the occurrence of an abnormal consumption by detecting that the actual energy usage that is outside the control limits. This may be done, for example, by first adding the predictions from the VDBB model, seasonal factors and ARIMA models to obtain the predicted usage for a time point (along with the UCL and LCL), then comparing the actual usage with the control bounds of the predicted usage.” [0033] FIG. 4A shows anomaly detection table. Predicted usage 404 is shown for a time period 410 with upper 406 and lower 408 bounds. Actual usage 402 during that period 410 is also shown.) receive a current dataset (See secondary reference) that includes the current value (See secondary reference) for the data element; (Jiang [0032] “The methodology of the present disclosure detects the occurrence of an abnormal consumption by detecting that the actual energy usage that is outside the control limits. This may be done, for example, by first adding the predictions from the VDBB model, seasonal factors and ARIMA models to obtain the predicted usage for a time point (along with the UCL and LCL), then comparing the actual usage with the control bounds of the predicted usage.” [0033] FIG. 4A shows anomaly detection table. Predicted usage 404 is shown for a time period 410 with upper 406 and lower 408 bounds. Actual usage 402 during that period 410 is also shown.) and generate an output that indicates whether the current value (See secondary reference) for the data element is within the predicted range defined by the upper threshold and the lower threshold. (Jiang [0032] “In the example shown in FIG. 3, it can be seen that energy uses at 302 and 304 are outside the bounds. For example, the energy use at 302 is below the lower control bound while the energy use at 304 is above the upper control bound. These are identified as the abnormal energy use. The abnormal energy uses below the lower control bound suggest some energy saving behaviors. It would be of interest to investigate such behaviors and carry out the behaviors in the future. On the other hand, abnormal energy uses above the upper control bounds indicate energy waste and require further investigation. Based on the investigation, wasteful usage may be avoided or reduced.”)
Jiang does not explicitly recite a current dataset or value.
However Palani discloses a current dataset and value. (Palani [0025] “The time series data 104 can be provided to the anomaly detection system 102 in real-time (e.g., continuously), approximately real-time, and/or in batches (e.g., intermittently), according to various embodiments.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize current data as per Palani for the analysis in Jiang in order to allow for “analyzing real-time data for the purposes of anomaly detection.” (Palani [0026])
Regarding Claim 2: The reference discloses The system of claim 1, wherein the statistical model is an auto-regressive integrated moving average model. (Jiang. [0003] “The method may yet further include generating a time series model for the error term to model seasonal factors which reflect monthly dependence on energy use and an auto-regressive integrated moving average model (ARIMA) which reflects temporal dependent patterns of the energy use.”)
Regarding Claim 4: The reference discloses The system of claim 1, wherein the one or more processors are further configured to: detect, among the historical values for the data element, one or more historical values associated with residual outliers associated with residual values outside the confidence interval; remove the residual outliers from a set of values that are used to calculate one or more of the confidence interval or the one or more statistical summaries; (Jiang [0027] “if M(t)=k and 0 otherwise. .zeta..sub.it represents the residual of the error term (remaining after the seasonal factors are removed). After removing the resultant seasonal factors from .epsilon..sub.ik, we further model the residuals via the autoregressive integrated moving average model (ARIMA)”) and forward-fill the residual values associated with the removed residual outliers in the set of values used to calculate one or more of the confidence interval or the one or more statistical summaries. (Jiang “[0032] The methodology of the present disclosure in one embodiment employs the time series model 106, and derives the upper control limit (UCL) and lower control limit (LCL), which can be used for anomaly detection. Specifically, for example, a 95% confidence interval can be constructed at each time point based on the ARIMA model. The UCL is thus obtained as the collection of the upper end points of the 95% confidence intervals and the LCL is the collection of the lower end points of the 95% confidence intervals. The methodology of the present disclosure detects the occurrence of an abnormal consumption by detecting that the actual energy usage that is outside the control limits. This may be done, for example, by first adding the predictions from the VDBB model, seasonal factors and ARIMA models to obtain the predicted usage for a time point (along with the UCL and LCL), then comparing the actual usage with the control bounds of the predicted usage.”)
Regarding Claim 5: The reference discloses The system of claim 1, wherein the one or more processors are further configured to: determine that the current value for the data element is associated with a residual value that is outside the confidence interval; and update, after one or more subsequent updates that include new values for the data element, the upper threshold and the lower threshold defining the confidence interval based on whether the residual value that is outside the confidence interval is an outlier. (Jiang [0027] “if M(t)=k and 0 otherwise. .zeta..sub.it represents the residual of the error term (remaining after the seasonal factors are removed). After removing the resultant seasonal factors from .epsilon..sub.ik, we further model the residuals via the autoregressive integrated moving average model (ARIMA)” Examiner Notes the seasonal factors which are removed represent the claimed “residual value that is outside the confidence interval”)
Regarding Claim 6: The reference discloses The system of claim 1, wherein the one or more processors are further configured to: and update, using the statistical model, the upper threshold and the lower threshold defining the confidence interval based on the update to the one or more statistical summaries. (Jiang “[0032] The methodology of the present disclosure in one embodiment employs the time series model 106, and derives the upper control limit (UCL) and lower control limit (LCL), which can be used for anomaly detection. Specifically, for example, a 95% confidence interval can be constructed at each time point based on the ARIMA model. The UCL is thus obtained as the collection of the upper end points of the 95% confidence intervals and the LCL is the collection of the lower end points of the 95% confidence intervals. The methodology of the present disclosure detects the occurrence of an abnormal consumption by detecting that the actual energy usage that is outside the control limits. This may be done, for example, by first adding the predictions from the VDBB model, seasonal factors and ARIMA models to obtain the predicted usage for a time point (along with the UCL and LCL), then comparing the actual usage with the control bounds of the predicted usage.”)
Jiang does not explicitly recite update the one or more statistical summaries for the data element based on the current value for the data element;
However Palani discloses update the one or more statistical summaries for the data element based on the current value for the data element; (Palani [0025] “The time series data 104 can be provided to the anomaly detection system 102 in real-time (e.g., continuously), approximately real-time, and/or in batches (e.g., intermittently), according to various embodiments.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize updating data as per Palani for the analysis in Jiang in order to allow for “analyzing real-time data for the purposes of anomaly detection.” (Palani [0026])
Regarding Claim 7: The reference discloses The system of claim 1, wherein the output includes a notification that is provided to a client device to trigger a data analyst review based on the current value for the data element falling outside the predicted range defined by the upper threshold and the lower threshold. (Jiang [0032] “In the example shown in FIG. 3, it can be seen that energy uses at 302 and 304 are outside the bounds. For example, the energy use at 302 is below the lower control bound while the energy use at 304 is above the upper control bound. These are identified as the abnormal energy use. The abnormal energy uses below the lower control bound suggest some energy saving behaviors. It would be of interest to investigate such behaviors and carry out the behaviors in the future. On the other hand, abnormal energy uses above the upper control bounds indicate energy waste and require further investigation. Based on the investigation, wasteful usage may be avoided or reduced.”)
Regarding Claim 8: The reference discloses The system of claim 1, wherein the output includes a first plot to indicate actual values for the data element over a time period and a second plot to indicate, relative to the confidence interval, residual values corresponding to differences between the actual values for the data element and predicted values for the data element over the time period. (Jiang [0032] “The methodology of the present disclosure detects the occurrence of an abnormal consumption by detecting that the actual energy usage that is outside the control limits. This may be done, for example, by first adding the predictions from the VDBB model, seasonal factors and ARIMA models to obtain the predicted usage for a time point (along with the UCL and LCL), then comparing the actual usage with the control bounds of the predicted usage.” [0033] FIG. 4A shows anomaly detection table. Predicted usage 404 is shown for a time period 410 with upper 406 and lower 408 bounds. Actual usage 402 during that period 410 is also shown.)
Regarding Claim 9: Jiang does not explicitly recite The system of claim 1, wherein the historical values and the current value for the data element are stored as structured data in a data repository that is updated at periodic intervals.
However Palani recites The system of claim 1, wherein the historical values and the current value for the data element are stored as structured data in a data repository that is updated at periodic intervals. ([0025] The time series data 104 can be data generated by, for example, routers, hubs, modems, storage volumes, servers, databases, applications, and the like. The time series data 104 can be numerical data, textual data, or a combination of both, such as, but not limited to, logfile data. The time series data 104 can be received from (or be consistent with) data that would be utilized by, for example, any Security Information and Event Management (SIEM) system. In some embodiments, the time series data 104 are associated with a plurality of similar or dissimilar devices (or virtual manifestations of devices) such as tens, hundreds, or thousands of devices. In some embodiments, the time series data 104 includes numerous continuous data streams (e.g., time-varying data streams that are consistently creating new data). In some embodiments, the time series data 104 can be univariate data (e.g., insofar as a single variable may be changing over time) and/or as multi-model data (e.g., insofar as multiple distinct streams of similar or dissimilar data can be included in time series data 104). The time series data 104 can be provided to the anomaly detection system 102 in real-time (e.g., continuously), approximately real-time, and/or in batches (e.g., intermittently), according to various embodiments.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize updating data as per Palani for the analysis in Jiang in order to allow for “analyzing real-time data for the purposes of anomaly detection.” (Palani [0026])
Regarding Claim 16: The reference discloses A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a data quality system, cause the data quality system to: obtain, from a data repository that is updated at periodic intervals, a historical dataset that includes historical values for a data element, wherein the historical values for the data element are stored as structured data in the data repository; (Jiang [0003] A method and system for performing analysis of energy consumption in one or more buildings may be provided. The analysis may include anomaly detection, forecasting or root cause analysis of energy consumption, or combinations thereof, for a portfolio of buildings using multi-step statistical modeling. The method, in one aspect, may include receiving energy consumption data associated with a building, receiving building characteristic data associated with the building, receiving building operation and activities data associated with the building, and receiving weather data.) generate one or more statistical summaries for the data element based on the historical values for the data element; (Jiang [0003] “The method may also include fitting said energy consumption data, said building characteristic data, said building operation and activities data, and said weather data to generate a variable based degree model.”) generate, using a statistical model, a confidence interval defined by an upper threshold and a lower threshold based on the one or more statistical summaries, wherein the upper threshold and the lower threshold define a predicted range for a current value (See secondary reference) for the data element; (Jiang [0032] “The methodology of the present disclosure detects the occurrence of an abnormal consumption by detecting that the actual energy usage that is outside the control limits. This may be done, for example, by first adding the predictions from the VDBB model, seasonal factors and ARIMA models to obtain the predicted usage for a time point (along with the UCL and LCL), then comparing the actual usage with the control bounds of the predicted usage.” [0033] FIG. 4A shows anomaly detection table. Predicted usage 404 is shown for a time period 410 with upper 406 and lower 408 bounds. Actual usage 402 during that period 410 is also shown.) receive, based on an update to the structured data in the data repository, a current dataset (See secondary reference) that includes the current value (See secondary reference) for the data element; (Jiang [0032] “The methodology of the present disclosure detects the occurrence of an abnormal consumption by detecting that the actual energy usage that is outside the control limits. This may be done, for example, by first adding the predictions from the VDBB model, seasonal factors and ARIMA models to obtain the predicted usage for a time point (along with the UCL and LCL), then comparing the actual usage with the control bounds of the predicted usage.” [0033] FIG. 4A shows anomaly detection table. Predicted usage 404 is shown for a time period 410 with upper 406 and lower 408 bounds. Actual usage 402 during that period 410 is also shown.) and generate an output that indicates whether the current value (See secondary reference) for the data element is within the predicted range defined by the upper threshold and the lower threshold. (Jiang [0032] “In the example shown in FIG. 3, it can be seen that energy uses at 302 and 304 are outside the bounds. For example, the energy use at 302 is below the lower control bound while the energy use at 304 is above the upper control bound. These are identified as the abnormal energy use. The abnormal energy uses below the lower control bound suggest some energy saving behaviors. It would be of interest to investigate such behaviors and carry out the behaviors in the future. On the other hand, abnormal energy uses above the upper control bounds indicate energy waste and require further investigation. Based on the investigation, wasteful usage may be avoided or reduced.”)
Jiang does not explicitly recite a current value or dataset.
However Palani discloses a current value and dataset. (Palani [0025] “The time series data 104 can be provided to the anomaly detection system 102 in real-time (e.g., continuously), approximately real-time, and/or in batches (e.g., intermittently), according to various embodiments.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize current data as per Palani for the analysis in Jiang in order to allow for “analyzing real-time data for the purposes of anomaly detection.” (Palani [0026])
Regarding Claim 17: The reference discloses The non-transitory computer-readable medium of claim 16, wherein the one or more instructions further cause the data quality system to: detect, among the historical values for the data element, one or more historical values associated with residual outliers associated with residual values outside the confidence interval; remove the residual outliers from a set of values that are used to calculate one or more of the confidence interval or the one or more statistical summaries; (Jiang [0027] “if M(t)=k and 0 otherwise. .zeta..sub.it represents the residual of the error term (remaining after the seasonal factors are removed). After removing the resultant seasonal factors from .epsilon..sub.ik, we further model the residuals via the autoregressive integrated moving average model (ARIMA)”) and forward-fill the residual values associated with the removed residual outliers in the set of values used to calculate one or more of the confidence interval or the one or more statistical summaries. (Jiang “[0032] The methodology of the present disclosure in one embodiment employs the time series model 106, and derives the upper control limit (UCL) and lower control limit (LCL), which can be used for anomaly detection. Specifically, for example, a 95% confidence interval can be constructed at each time point based on the ARIMA model. The UCL is thus obtained as the collection of the upper end points of the 95% confidence intervals and the LCL is the collection of the lower end points of the 95% confidence intervals. The methodology of the present disclosure detects the occurrence of an abnormal consumption by detecting that the actual energy usage that is outside the control limits. This may be done, for example, by first adding the predictions from the VDBB model, seasonal factors and ARIMA models to obtain the predicted usage for a time point (along with the UCL and LCL), then comparing the actual usage with the control bounds of the predicted usage.”)
Regarding Claim 18: The reference discloses The non-transitory computer-readable medium of claim 16, wherein the one or more instructions further cause the data quality system to: determine that the current value for the data element is associated with a residual value that is outside the confidence interval; and update, after one or more subsequent updates that include new values for the data element, the upper threshold and the lower threshold defining the confidence interval based on whether the residual value that is outside the confidence interval is an outlier. (Jiang [0027] “if M(t)=k and 0 otherwise. .zeta..sub.it represents the residual of the error term (remaining after the seasonal factors are removed). After removing the resultant seasonal factors from .epsilon..sub.ik, we further model the residuals via the autoregressive integrated moving average model (ARIMA)” Examiner Notes the seasonal factors which are removed represent the claimed “residual value that is outside the confidence interval”)
Regarding Claim 19: The reference discloses The non-transitory computer-readable medium of claim 16, wherein the output includes a notification that is provided to a client device to trigger a data analyst review based on the current value for the data element falling outside the predicted range defined by the upper threshold and the lower threshold. (Jiang [0032] “In the example shown in FIG. 3, it can be seen that energy uses at 302 and 304 are outside the bounds. For example, the energy use at 302 is below the lower control bound while the energy use at 304 is above the upper control bound. These are identified as the abnormal energy use. The abnormal energy uses below the lower control bound suggest some energy saving behaviors. It would be of interest to investigate such behaviors and carry out the behaviors in the future. On the other hand, abnormal energy uses above the upper control bounds indicate energy waste and require further investigation. Based on the investigation, wasteful usage may be avoided or reduced.”)
Regarding Claim 20: The reference discloses The non-transitory computer-readable medium of claim 16, wherein the output includes a visualization that indicates whether the current value for the data element is within the predicted range defined by the upper threshold and the lower threshold. (Jiang. Figure 4A)
5. Claim(s) 3 and 10-15 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No. 20120278051, hereafter Jiang in view of U.S. Patent Publication No. 20210344695, hereafter Palani further in view of U.S. Patent No. 10127234, hereafter Krishnan.
Regarding Claim 3: Jiang and Palani do not explicitly recite The system of claim 1, wherein the statistical model used to generate the confidence interval applies weights to the historical values for the data element that are progressively heavier for more recent historical values.
However Krishnan discloses wherein the statistical model used to generate the confidence interval applies weights to the historical values for the data element that are progressively heavier for more recent historical values. (Krishnan. Column 21, Lines 32-35, “In one embodiment, the metrics may be assigned weights based on how recently they were collected—e.g., metrics collected during the most recent ten minutes may be assigned a higher weight than metrics collected more than an hour ago.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize the weighting scheme of Krishnan for the calculations in Jiang and Palani since “weights based on how recently the metrics were collected, e.g., under the assumption that more recent metrics are likely to be better predictors of accesses in the near future than metrics that were collected earlier.” (Krishnan. Column 21, Lines 52-55)
Regarding Claim 10: The reference discloses A method for automated data quality monitoring and data governance, comprising:
obtaining, by a data quality system, a historical dataset that includes historical values for a data element; (Jiang [0003] A method and system for performing analysis of energy consumption in one or more buildings may be provided. The analysis may include anomaly detection, forecasting or root cause analysis of energy consumption, or combinations thereof, for a portfolio of buildings using multi-step statistical modeling. The method, in one aspect, may include receiving energy consumption data associated with a building, receiving building characteristic data associated with the building, receiving building operation and activities data associated with the building, and receiving weather data.)
generating, by the data quality system, one or more statistical summaries for the data element based on the historical values for the data element; (Jiang [0003] “The method may also include fitting said energy consumption data, said building characteristic data, said building operation and activities data, and said weather data to generate a variable based degree model.”)
generating, by the data quality system, using an auto-regressive integrated moving average (ARIMA) model, a confidence interval defined by an upper threshold and a lower threshold based on the one or more statistical summaries, (Jiang [0003] “The method may yet further include generating a time series model for the error term to model seasonal factors which reflect monthly dependence on energy use and an auto-regressive integrated moving average model (ARIMA) which reflects temporal dependent patterns of the energy use.”)
wherein the upper threshold and the lower threshold define a predicted range for a current value (See secondary reference) for the data element, and (Jiang [0009] FIG. 3 illustrates a graph that shows both the predicted usage and actual usage plotted with upper and lower bounds.)
receiving, by the data quality system, a current dataset (See secondary reference) that includes the current value (See secondary reference) for the data element; and (Jiang [0032] “The methodology of the present disclosure detects the occurrence of an abnormal consumption by detecting that the actual energy usage that is outside the control limits. This may be done, for example, by first adding the predictions from the VDBB model, seasonal factors and ARIMA models to obtain the predicted usage for a time point (along with the UCL and LCL), then comparing the actual usage with the control bounds of the predicted usage.” [0033] FIG. 4A shows anomaly detection table. Predicted usage 404 is shown for a time period 410 with upper 406 and lower 408 bounds. Actual usage 402 during that period 410 is also shown.)
generating, by the data quality system, an output that indicates whether the current value (See secondary reference) for the data element is within the predicted range defined by the upper threshold and the lower threshold. (Jiang [0032] “In the example shown in FIG. 3, it can be seen that energy uses at 302 and 304 are outside the bounds. For example, the energy use at 302 is below the lower control bound while the energy use at 304 is above the upper control bound. These are identified as the abnormal energy use. The abnormal energy uses below the lower control bound suggest some energy saving behaviors. It would be of interest to investigate such behaviors and carry out the behaviors in the future. On the other hand, abnormal energy uses above the upper control bounds indicate energy waste and require further investigation. Based on the investigation, wasteful usage may be avoided or reduced.”)
Jiang does not explicitly recite a current value or dataset.
However Palani discloses a current value and dataset. (Palani [0025] “The time series data 104 can be provided to the anomaly detection system 102 in real-time (e.g., continuously), approximately real-time, and/or in batches (e.g., intermittently), according to various embodiments.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize current data as per Palani for the analysis in Jiang in order to allow for “analyzing real-time data for the purposes of anomaly detection.” (Palani [0026])
Jiang and Palani do not recite wherein the ARIMA model applies weights to the historical values for the data element that are progressively heavier for more recent historical values;
However Krishnan discloses wherein the ARIMA model applies weights to the historical values for the data element that are progressively heavier for more recent historical values; (Krishnan. Column 21, Lines 32-35, “In one embodiment, the metrics may be assigned weights based on how recently they were collected—e.g., metrics collected during the most recent ten minutes may be assigned a higher weight than metrics collected more than an hour ago.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize the weighting scheme of Krishnan for the calculations in Jiang and Palani since “weights based on how recently the metrics were collected, e.g., under the assumption that more recent metrics are likely to be better predictors of accesses in the near future than metrics that were collected earlier.” (Krishnan. Column 21, Lines 52-55)
Regarding Claim 11: The reference discloses The method of claim 10, further comprising: detecting, among the historical values for the data element, one or more historical values associated with residual outliers associated with residual values outside the confidence interval; removing the residual outliers from a set of values that are used to calculate one or more of the confidence interval or the one or more statistical summaries; (Jiang [0027] “if M(t)=k and 0 otherwise. .zeta..sub.it represents the residual of the error term (remaining after the seasonal factors are removed). After removing the resultant seasonal factors from .epsilon..sub.ik, we further model the residuals via the autoregressive integrated moving average model (ARIMA)”) and forward-filling the residual values associated with the removed residual outliers in the set of values used to calculate one or more of the confidence interval or the one or more statistical summaries. (Jiang “[0032] The methodology of the present disclosure in one embodiment employs the time series model 106, and derives the upper control limit (UCL) and lower control limit (LCL), which can be used for anomaly detection. Specifically, for example, a 95% confidence interval can be constructed at each time point based on the ARIMA model. The UCL is thus obtained as the collection of the upper end points of the 95% confidence intervals and the LCL is the collection of the lower end points of the 95% confidence intervals. The methodology of the present disclosure detects the occurrence of an abnormal consumption by detecting that the actual energy usage that is outside the control limits. This may be done, for example, by first adding the predictions from the VDBB model, seasonal factors and ARIMA models to obtain the predicted usage for a time point (along with the UCL and LCL), then comparing the actual usage with the control bounds of the predicted usage.”)
Regarding Claim 12: The reference discloses The method of claim 10, further comprising: determining that the current value for the data element is associated with a residual value that is outside the confidence interval; and updating, after one or more subsequent updates that include new values for the data element, the upper threshold and the lower threshold defining the confidence interval based on whether the residual value that is outside the confidence interval is an outlier. (Jiang [0027] “if M(t)=k and 0 otherwise. .zeta..sub.it represents the residual of the error term (remaining after the seasonal factors are removed). After removing the resultant seasonal factors from .epsilon..sub.ik, we further model the residuals via the autoregressive integrated moving average model (ARIMA)” Examiner Notes the seasonal factors which are removed represent the claimed “residual value that is outside the confidence interval”)
Regarding Claim 13: Jiang discloses The method of claim 10, further comprising: updating, using the ARIMA model, the upper threshold and the lower threshold defining the confidence interval based on the update to the one or more statistical summaries. (Jiang “[0032] The methodology of the present disclosure in one embodiment employs the time series model 106, and derives the upper control limit (UCL) and lower control limit (LCL), which can be used for anomaly detection. Specifically, for example, a 95% confidence interval can be constructed at each time point based on the ARIMA model. The UCL is thus obtained as the collection of the upper end points of the 95% confidence intervals and the LCL is the collection of the lower end points of the 95% confidence intervals. The methodology of the present disclosure detects the occurrence of an abnormal consumption by detecting that the actual energy usage that is outside the control limits. This may be done, for example, by first adding the predictions from the VDBB model, seasonal factors and ARIMA models to obtain the predicted usage for a time point (along with the UCL and LCL), then comparing the actual usage with the control bounds of the predicted usage.”)
Jiang does not explicitly recite updating the one or more statistical summaries for the data element based on the current value for the data element.
However Palani discloses updating the one or more statistical summaries for the data element based on the current value for the data element. (Palani [0025] “The time series data 104 can be provided to the anomaly detection system 102 in real-time (e.g., continuously), approximately real-time, and/or in batches (e.g., intermittently), according to various embodiments.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize updating data as per Palani for the analysis in Jiang in order to allow for “analyzing real-time data for the purposes of anomaly detection.” (Palani [0026])
Regarding Claim 14: The reference discloses The method of claim 10, wherein the output includes a notification that is provided to a client device to trigger a data analyst review based on the current value for the data element falling outside the predicted range defined by the upper threshold and the lower threshold. (Jiang [0032] “In the example shown in FIG. 3, it can be seen that energy uses at 302 and 304 are outside the bounds. For example, the energy use at 302 is below the lower control bound while the energy use at 304 is above the upper control bound. These are identified as the abnormal energy use. The abnormal energy uses below the lower control bound suggest some energy saving behaviors. It would be of interest to investigate such behaviors and carry out the behaviors in the future. On the other hand, abnormal energy uses above the upper control bounds indicate energy waste and require further investigation. Based on the investigation, wasteful usage may be avoided or reduced.”)
Regarding Claim 15: The reference discloses The method of claim 10, wherein the output includes a first plot to indicate actual values for the data element over a time period and a second plot to indicate, relative to the confidence interval, residual values corresponding to differences between the actual values for the data element and predicted values for the data element over the time period. (Jiang [0032] “The methodology of the present disclosure detects the occurrence of an abnormal consumption by detecting that the actual energy usage that is outside the control limits. This may be done, for example, by first adding the predictions from the VDBB model, seasonal factors and ARIMA models to obtain the predicted usage for a time point (along with the UCL and LCL), then comparing the actual usage with the control bounds of the predicted usage.” [0033] FIG. 4A shows anomaly detection table. Predicted usage 404 is shown for a time period 410 with upper 406 and lower 408 bounds. Actual usage 402 during that period 410 is also shown.)
Conclusion
6. All Claims are rejected.
7. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
i) U.S. Patent Publication No. 20220200878
ii) U.S. Patent Publication No. 20100082405
iii) Song, Hongtao, et al. "Autoregressive integrated moving average model–based secure data aggregation for wireless sensor networks." International Journal of Distributed Sensor Networks 16.3 (2020): 1550147720912958.
iv) Thiyagarajan, Karthick, Sarath Kodagoda, and Linh Van Nguyen. "Predictive analytics for detecting sensor failure using autoregressive integrated moving average model." 2017 12th IEEE conference on industrial electronics and applications (ICIEA). IEEE, 2017.
8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Saif A. Alhija whose telephone number is (571) 272-8635. The examiner can normally be reached on M-F, 10:00-6:00.
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, Renee Chavez, can be reached at (571) 270-1104. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300. Informal or draft communication, please label PROPOSED or DRAFT, can be additionally sent to the Examiners fax phone number, (571) 273-8635.
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SAA
/SAIF A ALHIJA/Primary Examiner, Art Unit 2186
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