Detailed Action
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This is the initial office action based on the application filed on July 30th, 2024 which claim 1-20 have been presented for examination.
Status of Claims
2. Claims 1-20 are pending in the application, of which claims 1, 11 and 16 are in independent form and these claims (1-20) are subject to following rejection(s) and/or objection(s) set forth in the following Office Action below.
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., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1:
claims 1-10 (method), claims 11-15 (non-transitory computer-readable storage medium), and claims 16-20 (apparatus). Thus, they fall in statutory categories.
Step 2A Prong 1:
Claim 1 recites:
(a). obtaining live data, the live data comprising measurements indicating operational conditions for the one or more data processing systems;
(b). obtaining, from external data sources, forecasted data related to the operational conditions, the forecasted data being generated using proprietary methods;
(c). performing a sampling process, using the forecasted data, to obtain a mixed input data set, the mixed input data set comprising the live data and at least a portion of the forecasted data;
(d). making a determination, based on at least the mixed input data set, regarding whether a policy of a set of existing policies is invoked, the policy comprising an action set usable to update operation of the one or more data processing systems;
(e). performing the action set to update the operation of the one or more data processing systems; and
(f). providing, based on the updated operation of the one or more data processing systems, computer-implemented services.
Step 2A, Prong 1
(a)-(f) can be done human mind and/or by human with pen and paper, i.e., (a-b) acquiring or collecting data/information on a printed paper, (c) physically comparing data and mixing data by viewing and/or reviewing extended list, (d) is evaluation or judgement, (e) providing a decision based on the extended data/information to be followed, (f) is insignificant extra-solution activity. Thus, the claim as a whole does not integrate the exception into a practical application.
Step 2B
The additional elements, considering them both individually and in combination, do not amount to significantly more than the judicial exception itself.
Claims 2, 12 and 17 recite:
measurements comprise quantifications of uncertainty and the forecasted data do not comprise quantifications of uncertainty; which is act of determination can be performed through observation, evaluation, judgement with the aid of a human using a pen, marker and a paper, and presumably viewing and/or reviewing extended list of alerts. As such, these limitations fall within the “Mental Processes” grouping of abstract ideas.
Claims 3, 13 and 18 recite:
a first sub-set comprising a first portion of the forecasted data and the live data; and a second sub-set comprising a second portion of the forecasted data and the live data; which generalized statement, and can be performed through observation, evaluation, judgement with the aid of a human using a pen, marker and a paper, and presumably viewing and/or reviewing extended list of datum. As such, these limitations fall within the “Mental Processes” grouping of abstract ideas.
Claims 4, 14 and 19 recite:
sampling process comprises randomly selecting the first portion of the forecasted data; performed through observation, evaluation, judgement with the aid of a human using a pen, marker and a paper, and presumably viewing and/or reviewing extended list of data. As such, these limitations fall within the “Mental Processes” grouping of abstract ideas.
Claims 5, 15 and 20 recites:
the first portion of the forecasted data comprises a first forecasted data value from a first external data source of the external data sources; and the second portion of the forecasted data comprises a second forecasted data value from a second external data source of the external data sources, the second forecasted data value representing a same condition at a same point in time as the first forecasted data value; these are generalized extended information tied to base claims that are insignificant extra-solution activity. Thus, elements of the claims, considered both individually and ‘as an ordered combination, do not add enough to transform the nature of the claim’ into a patent-eligible application.
Claim 6 recites:
using the first sub-set of the mixed input data set and at least one inference model to obtain a first prediction of a plurality of predictions; and using the second sub-set of the mixed input data set and the at least one inference model to obtain a second prediction of the plurality of predictions, wherein the plurality of predictions each indicate whether a future state will occur which can be done by aid of a human using a pen, marker and a paper, and presumably viewing and/or reviewing.
Claim 7 recites:
analyzing at least the plurality of predictions to obtain a statistical characterization regarding agreement in the at least the plurality of predictions; making a second determination regarding whether the statistical characterization meets criteria; and in an instance of the second determination in which the statistical characterization meets the criteria: concluding that the policy is invoked can be performed in the human mind through observation, evaluation, judgement, opinion with the aid of a human using a pen, marker and a paper, and presumably viewing and/or reviewing comment contents. As such, these limitations fall within the “Mental Processes” grouping of abstract ideas.
Claim 8 recites:
statistical characterization comprises at least one quantity selected from a group consisting of: a mean; a median; a mode; and a standard deviation these can be performed in the human mind through observation, evaluation, judgement, opinion with the aid of pen and paper. As such, this limitation falls within the “Mental Processes” grouping of abstract ideas. Therefore, claim 8 is ineligible.
Claim 9 recites:
performing an analysis process using the forecasted data to obtain a forecasted data statistical characterization, the forecasted data statistical characterization indicating variability in forecasted data values of the forecasted data; and using the forecasted data statistical characterization to obtain the mixed input data set so that the mixed input data set comprises a representation of the variability in the forecasted data values; these can be performed in the human mind through observation, evaluation, judgement, opinion with the aid of a human using a pen, marker and a paper, and presumably viewing and/or reviewing comment contents. As such, they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea, these limitations fall within the “Mental Processes” grouping of abstract ideas.
Claim 10 recites:
forecasted data values of the forecasted data are obtained from the external data sources that each use different forecasting models and/or different input data for forecasting models with respect to others of the external data sources, and each of the external data sources providing limited access to information regarding the forecasted data values; these can be performed in the human mind through observation, evaluation, judgement, opinion with the aid of a human using a pen, marker and a paper, and presumably viewing and/or reviewing comment contents. As such, they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea, these limitations fall within the “Mental Processes” grouping of abstract ideas.
In regards to independent claim 11 recites: A non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations for managing operation of one or more data processing systems including similar limitations 1, which invoke the same analysis as claim 1 above; thus, simply adding extra-solution activity or generic computer components does not integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. MPEP 2106.05. Therefore, claim 11 is ineligible.
In regards to independent claim 16 recites: A data processing system, comprising: a processor; and a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to perform operations for managing operation of one or more data processing systems including similar limitations 1, which invoke the same analysis as claim 1 above; thus, simply adding extra-solution activity or generic computer components does not integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. MPEP 2106.05. Therefore, claim 16 is ineligible.
Claim Rejections – 35 USC §103
4. 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 of this title, 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.
5. Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Dove et al. (US Patent Application Publication No. 2014/0164059 A1 -herein after Dove) in view of Roche et al. (US Patent Publication No. 11,853,187 B1 herein after Roche).
Per claim 1:
Dove discloses:
A method for managing operation of one or more data processing systems (At least see ¶[0051] steps in a method in accordance with one or more embodiments), the method comprising:
obtaining live data, the live data comprising measurements indicating operational conditions for the one or more data processing systems (At least see ¶[00013] - heuristic(s) on the new/incoming data);
obtaining, from external data sources, forecasted data related to the operational conditions, the forecasted data being generated using proprietary methods (At least see ¶[0003] - at least in part on the heuristic(s) associated with the historical set of data);
performing a sampling process, using the forecasted data, to obtain a mixed input data set, the mixed input data set comprising the live data and at least a portion of the forecasted data (At least see ¶[0013] - store the forecast(s) in a repository for future use, as further discussed below. Responsive to receiving new and/or incoming data, some embodiments generate heuristic(s) on the new/incoming data. As in the case of the historical data, the new/incoming data can be partitioned, and multiple heuristics can be generated for each new or additional partition).
Dove sufficiently discloses claimed limitations as set forth above, but Dove does not explicitly disclose: making a determination, based on at least the mixed input data set, regarding whether a policy of a set of existing policies is invoked, the policy comprising an action set usable to update operation of the one or more data processing systems; in an instance of the determination in which the policy is invoked: performing the action set to update the operation of the one or more data processing systems; and providing, based on the updated operation of the one or more data processing systems, computer-implemented services.
However, Roche discloses:
making a determination, based on at least the mixed input data set, regarding whether a policy of a set of existing policies is invoked, the policy comprising an action set usable to update operation of the one or more data processing systems (At least see Col. 3:22-26 - Identifying the suspect quantity may include performing rules based analysis for the first telemetry data, the rules based analysis indicating that the suspect quantity is not correlated with another quantity from the first telemetry data that is correlated with the suspect quantity);
in an instance of the determination in which the policy is invoked:
performing the action set to update the operation of the one or more data processing systems (At least see Col. 2:21-23 - Once an inferred health state of sufficiently high confidence is achieved, actions may be identified and used to update the operation of the data processing systems); and
providing, based on the updated operation of the one or more data processing systems, computer-implemented services (At least see Col. 2:31-34 - managing the operation of data processing systems that improved the likelihood of the data processing systems continuing to provide desired computer implemented services).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Roche into Dove’s invention because Roche’s teaching would provide confidence levels for the inferences, wherein confidence levels may be used to modify collection of telemetry data, and when an inference is generated for which the inference model has a low level of confidence in the inference, the telemetry data collection process for telemetry data upon which the inference is based may be modified, which the modified collection process increase the rate of collection of telemetry data, expand the types of collected telemetry data, and/or otherwise modify the telemetry data collection process that ultimately lead to manage operation of data processing systems (please see Col. 4:48-58).
Per claim 2:
Dove discloses:
measurements comprise quantifications of uncertainty and the forecasted data do not comprise quantifications of uncertainty (At least see ¶[0013] - the forecast quality metric can indicate whether an associated forecast had a high quality and/or degree of accuracy).
Per claim 3:
Dove discloses:
a first sub-set comprising a first portion of the forecasted data and the live data; and
a second sub-set comprising a second portion of the forecasted data and the live data (At least see ¶[0013] - a forecast can be generated from the heuristic(s) to project and/or anticipate future behavior(s) of the system and/or product. Some embodiments store the forecast(s) in a repository for future use, as further discussed below. Responsive to receiving new and/or incoming data, some embodiments generate heuristic(s) on the new/incoming data).
Per claim 4:
Dove discloses:
performing the sampling process comprises randomly selecting the first portion of the forecasted data (At least see ¶[0022] - heuristics are generated for the historical data as a whole set, while in other cases heuristics are generated for smaller portions and/or partitions of the historical data).
Per claim 5:
Dove discloses:
the first portion of the forecasted data comprises a first forecasted data value from a first external data source of the external data sources; and the second portion of the forecasted data comprises a second forecasted data value from a second external data source of the external data sources, the second forecasted data value representing a same condition at a same point in time as the first forecasted data value (At least see ¶[0034] - historical data 304, illustrated here as an input to data heuristics engine 302. In some embodiments, historical data 304 can reside in a data repository and/or memory located on a same computing device that hosts data heuristics engine 302. Alternately or additionally, historical data 304 can reside external to the computing device hosting data heuristics engine 302).
Per claim 6:
Dove discloses:
using the first sub-set of the mixed input data set and at least one inference model to obtain a first prediction of a plurality of predictions; and using the second sub-set of the mixed input data set and the at least one inference model to obtain a second prediction of the plurality of predictions, wherein the plurality of predictions each indicate whether a future state will occur (At least see ¶[0013] - forecast quality metric can indicate whether an associated forecast had a high quality and/or degree of accuracy, a low quality and/or degree of accuracy, and so forth, in predicting behavior(s). Responsive to determining a high quality and/or degree of accuracy, some embodiments store the new incoming data in a repository. Alternately or additionally, some embodiments trigger a notification based upon low quality accuracy metric(s) and can, in some cases, quarantine the new incoming data for further analysis before and/or instead of storing the new incoming data in the repository).
Per claim 7:
Dove discloses:
analyzing at least the plurality of predictions to obtain a statistical characterization regarding agreement in the at least the plurality of predictions; making a second determination regarding whether the statistical characterization meets criteria; and in an instance of the second determination in which the statistical characterization meets the criteria: concluding that the policy is invoked (At least see ¶[0013] - forecast quality metric can indicate whether an associated forecast had a high quality and/or degree of accuracy, a low quality and/or degree of accuracy, and so forth, in predicting behavior(s). Responsive to determining a high quality and/or degree of accuracy, some embodiments store the new incoming data in a repository. Alternately or additionally, some embodiments trigger a notification based upon low quality accuracy metric(s) and can, in some cases, quarantine the new incoming data for further analysis before and/or instead of storing the new incoming data in the repository).
Per claim 8:
Dove discloses:
statistical characterization comprises at least one quantity selected from a group consisting of: a mean; a median; a mode; and a standard deviation (At least see ¶[0038] - monitoring the incoming data in real-time, the associated network traffic latency is measured to have an average latency of 2 seconds, which falls outside of the acceptable 10% error range. As further discussed below, this monitoring mechanism can be utilized to notify interested parties of the deviation from expected behavior).
Per claim 9:
Dove discloses:
performing an analysis process using the forecasted data to obtain a forecasted data statistical characterization, the forecasted data statistical characterization indicating variability in forecasted data values of the forecasted data; and using the forecasted data statistical characterization to obtain the mixed input data set so that the mixed input data set comprises a representation of the variability in the forecasted data values (At least see ¶[0041] - Quality scoring module 320 represents functionality that performs this comparison between the incoming data (and/or associated heuristic) with the forecast models, and calculates a "forecast quality metric" to qualify this comparison. By way of example and not of limitation, quality scoring module 320 can calculate a variance value between a forecast value and a value generated from incoming data 314 as an indicator of how close the two values match. It is to be appreciated and understood that other types of forecast quality metrics can be used to qualify the comparison and/or forecast(s) without departing from the scope of the claimed subject matter, such as percentage of difference, frequency of deviance, degree of standard deviation, a time series associated with the time window being utilized, an average deviance of the forecast model versus the actual data, calculating a Gaussian distribution of errors).
Per claim 10:
Dove discloses:
forecasted data values of the forecasted data are obtained from the external data sources that each use different forecasting models and/or different input data for forecasting models with respect to others of the external data sources, and each of the external data sources providing limited access to information regarding the forecasted data values (At least see ¶[0034] - historical data 304, illustrated here as an input to data heuristics engine 302. In some embodiments, historical data 304 can reside in a data repository and/or memory located on a same computing device that hosts data heuristics engine 302. Alternately or additionally, historical data 304 can reside external to the computing device hosting data heuristics engine 302. Historical data 304 can comprise any suitable type of data associated with characterizing an object/product/service, interactions of a user with the object/product/service, interactions of the object/product/service with other component).
Per claim 11-20:
Limitation rendered in claims 11-20 are as similar as the claims 1-10 above; and therefore, rejected based on same rational.
CONCLUSION
6. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZIAUL A. CHOWDHURY whose telephone number is (571)270-7750. The examiner can normally be reached on 9:30PM 6:30PM Monday -Friday.
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/ZIAUL A CHOWDHURY/ Primary Examiner, Art Unit 2192
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