DETAILED ACTION
1. Claims 1-6, 8, 10-13, 15-18, and 21-25 have been presented for examination.
Claims 7, 9, 14, and 19-20 have been cancelled.
Claims 21-25 are newly added.
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 .
Response to Arguments
3. Applicant's arguments filed 4/8/26 have been fully considered but they are not persuasive.
i) Following Applicants arguments and amendments the previously presented 101 rejection is MAINTAINED. Applicants argue that their invention represents a “practical application by providing a specific technological improvement in network security, namely, an automated, real-time system for detecting and remediating network attacks” and that their claims are analogous to Example 47, claim 3 (2024 Subject Matter Eligibility Examples. The Examiner notes, as also represented below, that Applicants claims are not analogous to the example provided specifically with respect to the alleged improvement. Example 47 noted a remediation of the network attacks whereas Applicants claims result in either a notification or a visualization and the update of a data calculation. No actual improvement to a technology is recited as the subsequent step to reach the alleged improvement is not recited, a user is merely being notified or seeing a visual and then presumably carrying out the subsequent improvement which is not claimed. As per MPEP 2106.05(a): “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements...” Additionally, as discussed in 2106.05(a)(II) improvements to technology or technical fields, “an improvement in the abstract idea itself … is not an improvement in technology”. The present claims are nothing more than mere instructions to perform the steps of the abstract idea. The present claims, as drafted, do not transform the abstract idea into patent eligible subject matter. The present claims are not narrowly drawn such that the implementation of the computer is anything more than a tool to perform the steps of the abstract idea. As such the 101 rejection is MAINTAINED.
ii) Following Applicants arguments and amendments, particularly as per page 16 of Applicants response, the previously presented prior art rejection is WITHDRAWN.
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.
4. Claims 1-6, 8, 10-13, 15-18, and 21-25 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 1-6, 8, 10-13, 15-18, and 21-25 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, wherein at least one of the statistical summaries includes a table-level metric including a duplicate count, the duplicate count indicating a number of duplicate values in a table or a number of rows associated with a column;” 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 1, the limitation of “determine whether the duplicate count for the current dataset is within the predicted range defined by the upper threshold and the lower threshold;” 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, wherein at least one of the statistical summaries includes a table-level metric including a duplicate count, the duplicate count indicating a number of duplicate values in a table or a number of rows associated with a column, 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 “determining, by the data quality system, whether the duplicate count for the current dataset 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 at least one of the statistical summaries includes a table-level metric including a duplicate count, the duplicate count indicating a number of duplicate values in a table or a number of rows associated with a column, 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 “determine whether the duplicate count for the current dataset 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 “memories” or “a processor,” nothing in the claim element precludes the step from practically being performed in the mind.
Dependent claims 2-6, 8, 11-13, 15, 17-18, 21-25 further narrow the abstract ideas, identified in the independent claims as a mental process.
Dependent claims 2-6, 8, 11-13, 15, 17-18, 21-25 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 limitations in claim 1, “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;” and “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;” and “in response to determining that the duplicate count for the current dataset is outside the predicted range, automatically: generate a notification to a client device to trigger a data analyst review of the current dataset for data governance;” and “generate a visualization indicating that the duplicate count current value for the current dataset is outside the predicted range;” and “dynamically update the one or more statistical summaries and the confidence interval based on the current value for the data element.” as well as the limitation in claim 10, “obtaining, by a data quality system and 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;” and “receiving, by the data quality system and based on an update to the structured data in the data repository, a current dataset that includes the current value for the data element;” and “in response to determining that the duplicate count for the current dataset is outside the predicted range, automatically: generating a notification to a client device to trigger a data analyst review of the current dataset for data governance;”, and “generating a visualization indicating that the duplicate count for the current dataset is outside the predicted range;”, and “dynamically updating the one or more statistical summaries and the confidence interval based on 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;” and “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;” and “in response to determining that the duplicate count for the current dataset is outside the predicted range, automatically: generate a notification to a client device to trigger a data analyst review of the current dataset for data governance;” and “generate a visualization indicating that the duplicate count for the current dataset is outside the predicted range;” and “dynamically update the one or more statistical summaries and the confidence interval based on 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, 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;” and “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;” and “in response to determining that the duplicate count for the current dataset is outside the predicted range, automatically: generate a notification to a client device to trigger a data analyst review of the current dataset for data governance;” and “generate a visualization indicating that the duplicate count current value for the current dataset is outside the predicted range;” and “dynamically update the one or more statistical summaries and the confidence interval based on the current value for the data element.” as well as the limitation in claim 10, “obtaining, by a data quality system and 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;” and “receiving, by the data quality system and based on an update to the structured data in the data repository, a current dataset that includes the current value for the data element;” and “in response to determining that the duplicate count for the current dataset is outside the predicted range, automatically: generating a notification to a client device to trigger a data analyst review of the current dataset for data governance;”, and “generating a visualization indicating that the duplicate count for the current dataset is outside the predicted range;”, and “dynamically updating the one or more statistical summaries and the confidence interval based on 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;” and “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;” and “in response to determining that the duplicate count for the current dataset is outside the predicted range, automatically: generate a notification to a client device to trigger a data analyst review of the current dataset for data governance;” and “generate a visualization indicating that the duplicate count for the current dataset is outside the predicted range;” and “dynamically update the one or more statistical summaries and the confidence interval based on 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 2-6, 8, 11-13, 15, 17-18, 21-25 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, claim 1, “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;” and “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;” and “in response to determining that the duplicate count for the current dataset is outside the predicted range, automatically: generate a notification to a client device to trigger a data analyst review of the current dataset for data governance;” and “generate a visualization indicating that the duplicate count current value for the current dataset is outside the predicted range;” and “dynamically update the one or more statistical summaries and the confidence interval based on the current value for the data element.” as well as the limitation in claim 10, “obtaining, by a data quality system and 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;” and “receiving, by the data quality system and based on an update to the structured data in the data repository, a current dataset that includes the current value for the data element;” and “in response to determining that the duplicate count for the current dataset is outside the predicted range, automatically: generating a notification to a client device to trigger a data analyst review of the current dataset for data governance;”, and “generating a visualization indicating that the duplicate count for the current dataset is outside the predicted range;”, and “dynamically updating the one or more statistical summaries and the confidence interval based on 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;” and “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;” and “in response to determining that the duplicate count for the current dataset is outside the predicted range, automatically: generate a notification to a client device to trigger a data analyst review of the current dataset for data governance;” and “generate a visualization indicating that the duplicate count for the current dataset is outside the predicted range;” and “dynamically update the one or more statistical summaries and the confidence interval based on 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, 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;” and “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;” and “in response to determining that the duplicate count for the current dataset is outside the predicted range, automatically: generate a notification to a client device to trigger a data analyst review of the current dataset for data governance;” and “generate a visualization indicating that the duplicate count current value for the current dataset is outside the predicted range;” and “dynamically update the one or more statistical summaries and the confidence interval based on the current value for the data element.” as well as the limitation in claim 10, “obtaining, by a data quality system and 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;” and “receiving, by the data quality system and based on an update to the structured data in the data repository, a current dataset that includes the current value for the data element;” and “in response to determining that the duplicate count for the current dataset is outside the predicted range, automatically: generating a notification to a client device to trigger a data analyst review of the current dataset for data governance;”, and “generating a visualization indicating that the duplicate count for the current dataset is outside the predicted range;”, and “dynamically updating the one or more statistical summaries and the confidence interval based on 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;” and “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;” and “in response to determining that the duplicate count for the current dataset is outside the predicted range, automatically: generate a notification to a client device to trigger a data analyst review of the current dataset for data governance;” and “generate a visualization indicating that the duplicate count for the current dataset is outside the predicted range;” and “dynamically update the one or more statistical summaries and the confidence interval based on 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 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 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 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 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 mathematical concepts including mathematical formulas or equations as well as calculations.
Dependent claim 8 and 15 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 claims 21 further defines the type of statistical calculation which merely narrows the abstract idea identified as a mental process and/or mathematical concepts including mathematical formulas or equations as well as calculations.
Dependent claims 22 further defines the type of statistical calculation which merely narrows the abstract idea identified as a mental process and/or mathematical concepts including mathematical formulas or equations as well as calculations.
Dependent claims 23 further defines the type of statistical calculation which merely narrows the abstract idea identified as a mental process and/or mathematical concepts including mathematical formulas or equations as well as calculations.
Dependent claims 24 further defines the type of statistical calculation which merely narrows the abstract idea identified as a mental process and/or mathematical concepts including mathematical formulas or equations as well as calculations.
Dependent claims 25 further defines the type of statistical calculation which merely narrows the abstract idea identified as a mental process and/or mathematical concepts including mathematical formulas or equations as well as calculations.
v) Accordingly, claims 1-6, 8, 10-13, 15-18, and 21-25 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.
Allowable Subject Matter
5. Claims 1-6, 8, 10-13, 15-18, and 21-25 are allowable over the prior art of record pending resolving all intervening issues such as the 101 rejection above. The following is a statement of reasons for the indication of allowable subject matter:
Claim 1 recites: 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, 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; generate one or more statistical summaries for the data element based on the historical values for the data element, wherein at least one of the statistical summaries includes a table-level metric including a duplicate count, the duplicate count indicating a number of duplicate values in a table or a number of rows associated with a column; 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; 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; determine whether the duplicate count for the current dataset is within the predicted range defined by the upper threshold and the lower threshold; in response to determining that the duplicate count for the current dataset is outside the predicted range, automatically: generate a notification to a client device to trigger a data analyst review of the current dataset for data governance; and generate a visualization indicating that the duplicate count current value for the current dataset is outside the predicted range; and dynamically update the one or more statistical summaries and the confidence interval based on the current value for the data element.
Claim 10 recites: A method for automated data quality monitoring and data governance, comprising: obtaining, by a data quality system and 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; generating, by the data quality system, one or more statistical summaries for the data element based on the historical values for the data element; 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, wherein at least one of the statistical summaries includes a table-level metric including a duplicate count, the duplicate count indicating a number of duplicate values in a table or a number of rows associated with a column, and wherein the ARIMA model applies weights to the historical values for the data element that are progressively heavier for more recent historical values; receiving, by the data quality system and based on an update to the structured data in the data repository, a current dataset that includes the current value for the data element; determining, by the data quality system, whether the duplicate count for the current dataset is within the predicted range defined by the upper threshold and the lower threshold; in response to determining that the duplicate count for the current dataset is outside the predicted range, automatically: generating a notification to a client device to trigger a data analyst review of the current dataset for data governance; and generating a visualization indicating that the duplicate count for the current dataset is outside the predicted range; and dynamically updating the one or more statistical summaries and the confidence interval based on the current value for the data element.
Claim 16 recites: 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; generate one or more statistical summaries for the data element based on the historical values for the data element; 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 at least one of the statistical summaries includes a table-level metric including a duplicate count, the duplicate count indicating a number of duplicate values in a table or a number of rows associated with a column, wherein the upper threshold and the lower threshold define a predicted range for a current value for the data element; 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; determine whether the duplicate count for the current dataset is within the predicted range defined by the upper threshold and the lower threshold; in response to determining that the duplicate count for the current dataset is outside the predicted range, automatically: generate a notification to a client device to trigger a data analyst review of the current dataset for data governance; and generate a visualization indicating that the duplicate count for the current dataset is outside the predicted range; and dynamically update the one or more statistical summaries and the confidence interval based on the current value for the data element.
The closest prior art of record includes:
i) U.S. Patent Publication No. 20220200878 which teaches as per [0009] “detection of an anomaly contained in the data (a volume of data, such as a vast quantity of data to be accumulated over a prolonged period of time, time-series data).”
ii) U.S. Patent Publication No. 20100082405 which teaches as per [0008] “dynamic or updatable forecast for dynamic multi-period-ahead forecasts for a given time period. One embodiment provides a forecasting solution that makes not only one-month-ahead dynamic forecasts but also multi-period-ahead dynamic forecasts. Exemplary embodiments are useful for addressing long-range forecasting needs, such as in financial analysis or long lead time supply chain planning.”
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.
However, the closest prior art of record does not explicitly teach or render obvious the limitations above,
particularly in combination with the other limitations within the claims. The dependent claims are allowable for at least the same reasons as their respective independent claims.
Conclusion
6. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
7. All Claims are rejected.
8. 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.
9. 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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