Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This non-final Office action is in response to applicant’s communication received on September 04, 2024, wherein claims 1-7 are currently pending.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Regarding Step 1 (MPEP 2106.03) of the subject matter eligibility test per MPEP 2106.03, claims 1-5 are directed to a device (i.e. machine), claim 6 is directed to a method (i.e., process), and claim 7 is directed to non-transitory computer readable medium (i.e. product or article of manufacture). Accordingly, all claims are directed to one of the four statutory categories of invention.
(Under Step 2) The claimed invention is directed to an abstract idea without significantly more.
(Under Step 2A, Prong 1 (MPEP 2106.04)) The independent claims (1, 6) and dependent claims (2-5, 7) recite obtaining/getting/receiving/etc., information/data (where the information itself is abstract in nature – e.g. action/behavioral data/values), data analysis to and manipulation to determine more abstract information/data (e.g. predicting using mathematical concepts/calculations, deviations, threshold comparisons, etc.,), and providing/displaying this determined data for further analysis and decision-making. The claimed invention further uses mathematical steps to analyze and determine further data (calculation and using predictive models – which are mathematical in nature as shown in the claims and specification; also mathematical relationships and formulations).
The limitations of the independent claims (1, 6) and dependent claims (2-5, 7), under the broadest reasonable interpretation, covers methods of mathematical concepts (e.g. predicting using mathematical concepts/calculations, deviations, threshold comparisons, etc.,; calculation and using predictive models – which are mathematical in nature as shown in the claims and specification; also mathematical relationships and formulations) and mental processes (concepts performed in the human mind (including observation and evaluation – for, for example, weather, stock prices, etc.,)). If a claims limitation, under its broadest reasonable interpretation, covers the performance of the limitation as mathematical relationships, mathematical formulas or equations, mathematical calculations then it falls within the Mathematical concepts grouping of abstract ideas. (MPEP 2106.04; and also see 2019 Revised Patent Subject Matter Eligibility Guidance - Federal Register, Vol. 84, Vol. 4, January 07, 2019, pages 50-57). If claim limitations, under its broadest reasonable interpretation, cover the performance of the limitation as concepts performed in the human mind (including an observation, evaluation, judgment, opinion), the claim limitations fall within the Mental process grouping of abstract ideas. (MPEP 2106.04; and also see 2019 Revised Patent Subject Matter Eligibility Guidance - Federal Register, Vol. 84, Vol. 4, January 07, 2019, pages 50-57).
Accordingly, since Applicant's claims fall under mathematical concepts grouping and mental processes grouping, the claims recite an abstract idea.
(Under Step 2A, prong 2 (MPEP 2106.04(d))) This judicial exception is not integrated into a practical application because but for the recitation of generic/general-purpose computers and/or computing components/elements/devcies/etc., (for example, devices, processors, memories, machine learning (only stated in an “apply it” fashion without any technical details and specifics), etc., (in Independent claim 1 and its dependent claims 2-5); no technical and/or computing elements at all states – just pure abstract idea (in independent claim 6); and non-transitory computer-readable medium, computer, etc., (dependent claim 7)) in the context of the claims, the claim encompasses the above stated abstract idea (mathematical concepts (e.g. predicting using mathematical concepts/calculations, deviations, threshold comparisons, etc.,; calculation and using predictive models – which are mathematical in nature as shown in the claims and specification; also mathematical relationships and formulations) and mental processes (concepts performed in the human mind (including observation and evaluation – for, for example, weather, stock prices, etc.,)). As shown above, the independent claims (1, 6) and dependent claims (2-5, 7) and specification recite generic/general-purpose computers and/or computing components/elements/devcies/etc., which are recited at a high level of generality performing generic/general-purpose computer/computing functions. (MPEP 2106.04; and also see 2019 Revised Patent Subject Matter Eligibility Guidance – Federal Register, Vol. 84, Vol. 4, January 07, 2019, page 53-55). The generic/general-purpose computers and/or computing components/elements/devcies/etc., limitations are no more than mere instructions to apply the judicial exception (the above abstract idea) in an apply-it fashion using generic/general-purpose computers, processors, and/or computer components/elements/ devices, etc. The CAFC has stated that it is not enough, however, to merely improve abstract processes by invoking a computer merely as a tool. Customedia Techs., LLC v. Dish Network Corp., 951 F.3d 1359, 1364 (Fed. Cir. 2020). The focus of the claims is simply to use computers and a familiar network as a tool to perform abstract processes involving simple information exchange. Carrying out abstract processes involving information exchange is an abstract idea. See, e.g., BSG, 899 F.3d at 1286; SAP America, 898 F.3d at 1167-68; Affinity Labs of Tex., LLC v. DIRECTV, LLC, 838 F.3d 1253, 1261-62 (Fed. Cir. 2016). And use of standard computers and networks to carry out those functions—more speedily, more efficiently, more reliably—does not make the claims any less directed to that abstract idea. See Alice Corp., 573 U.S. at 222-25; Customedia, 951 F.3d at 1364; Trading Techs. Int'l, Inc. v. IBG LLC, 921 F.3d 1084, 1092-93 (Fed. Cir. 2019); SAP America, 898 F.3d at 1167; Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1314 (Fed. Cir. 2016); Electric Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1353, 1355 (Fed. Cir. 2016); Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 1370 (Fed. Cir. 2015); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014). Accordingly, the additional elements do not integrate the abstract idea in to a practical application because it does not impose any meaningful limits on practicing the abstract idea – i.e. they are just post-solution/extra-solution activities.
(Under Step 2B (MPEP 2106.05)) The independent claims (1, 6) and dependent claims (2-5, 7) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims do not recite an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. The independent claims (1, 6) and dependent claims (2-5, 7) recite using known and/or generic/general-purpose computers and/or computing components/elements/devcies/etc., (for example, devices, processors, memories, machine learning (only stated in an “apply it” fashion without any technical details and specifics), etc., (in Independent claim 1 and its dependent claims 2-5); no technical and/or computing elements at all states – just pure abstract idea (in independent claim 6); and non-transitory computer-readable medium, computer, etc., (dependent claim 7)) and software. For the role of a computer in a computer implemented invention to be deemed meaningful in the context of this analysis, it must involve more than performance of "well-understood, routine, [and] conventional activities previously known to the industry." Alice Corp. v. CLS Bank Int'l, 110 USPQ2d 1976 (U.S. 2014), at 2359 (quoting Mayo, 132 S. Ct. at 1294 (internal quotation marks and brackets omitted)). These activities as claimed by the Applicant are all well-known and routine tasks in the field of art – as can been seen in the specification of Applicant’s application (for example, see Applicant’s specification at, for example, Figs. 1-2 and paras. 0016-0021 [general-purpose/generic computers/processors/etc., and generic/general-purpose computing components/devices/etc.,]) and/or the specification of the below cited art (used in the rejection below and on the PTO-892) and/or also as noted in the court cases in §2106.05 in the MPEP. Further, "the mere recitation of a generic computer cannot transform a patent ineligible abstract idea into a patent-eligible invention." Alice, at 2358. None of the hardware offers a meaningful limitation beyond generally linking the system to a particular technological environment, that is, implementation via computers. Adding generic computer components to perform generic functions that are well‐understood, routine and conventional, such as gathering data, performing calculations, and outputting a result would not transform the claim into eligible subject matter. Abstract ideas are excluded from patent eligibility based on a concern that monopolization of the basic tools of scientific and technological work might impede innovation more than it would promote it. The independent claims (1, 6) and dependent claims (2-5, 7) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims require no more than a generic computer to perform generic computer functions. The additional element(s) or combination of elements in the independent claims (1, 6) and dependent claims (2-5, 7) other than the abstract idea per se amount(s) to no more than: (i) mere instructions to implement the idea on a computer, and/or (ii) recitation of generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry. Applicant is directed to the following citations and references: Digitech Image., LLC v. Electronics for Imaging, Inc.(U.S. Patent No. 6,128,415); and (2) Federal register/Vol. 79, No 241 issued on December 16, 2014, page 74629, column 2, Gottschalk v. Benson. Viewed as a whole, the claims do not purport to improve the functioning of the computer itself, or to improve any other technology or technical field. Use of an unspecified, generic computer does not transform an abstract idea into a patent-eligible invention. Thus, the claim does not amount to significantly more than the abstract idea itself. See Alice Corp. v. CLS Bank Int'l, 110 USPQ2d 1976 (U.S. 2014).
The dependent claims (2-5, 7) further define the independent claims and merely narrow the described abstract idea, but not adding significantly more than the abstract idea. The above rejection includes and details the discussion of dependent claims and the above rejection applies to all the dependent claim limitations. In summary, the dependent claims further state using obtained data/information (where the information itself is abstract in nature), data analysis/manipulation to determine more data/information (heavily using mathematics), possibly obtaining more abstract information/data, and providing this determined data/information for further analysis and decision making (e.g. in weather, stock market, etc.,). The claimed invention further uses mathematical steps to analyze and determine further data (predictive models, value calculation with time, timing calculations, etc.,). These claims are directed towards mathematical concepts (e.g. predicting using mathematical concepts/calculations, deviations, threshold comparisons, etc.,; calculation and using predictive models – which are mathematical in nature as shown in the claims and specification; also mathematical relationships and formulations) and mental processes (concepts performed in the human mind (including observation and evaluation – for, for example, weather, stock prices, etc.,)). This judicial exception is not integrated into a practical application because the claims and specification recite generic/general-purpose computers and/or computing components/elements/devcies/etc., (for example, devices, processors, memories, machine learning (only stated in an “apply it” fashion without any technical details and specifics), etc., (in Independent claim 1 and its dependent claims 2-5); no technical and/or computing elements at all states – just pure abstract idea (in independent claim 6); and non-transitory computer-readable medium, computer, etc., (dependent claim 7)) which are recited at a high level of generality performing generic computer functions. (MPEP 2106.04 and also see 2019 Revised Patent Subject Matter Eligibility Guidance – Federal Register, Vol. 84, Vol. 4, January 07, 2019, page 53-55). The dependent claims also merely recites post-solution/extra-solution activities (with generic/general-purpose computers and/or computing components/devices/etc.,). The additional elements do not integrate the abstract idea in to a practical application because it does not impose any meaningful limits on practicing the abstract idea – i.e. they are just post-solution/extra-solution activities. The dependent claims merely use the same general technological environment and instructions to implement the abstract idea without adding any new additional elements. Also, the dependent claims also do not include additional elements that are sufficient to amount to significantly more than the juridical exception because the additional elements either individually or in combination are merely an extension of the abstract idea itself. See detailed rejection above.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-6 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Gras et al., (US 2015/0088719).
As per claim 1, Gras discloses a timing calculation device comprising: a processor; and a memory storing program instructions that cause the processor (¶¶ 0018 [processor or other computing device having a processor and memory…processor including program instructions], 0025 [predictive device; see with 0035-0039 [system…minutes to days, or even monthly or yearly periods…device…time intervals]], 0046; claim 1 of Gras) to:
input a first initial value and a second initial value that are necessary for predicting a future event that exhibits chaotic behavior or is likely to exhibit the chaotic behavior (¶¶ 0045-0052 [predicting the likely occurrence of a chaotic future or trend… a baseline set of historical data is collected over a preset initial sampling or monitoring period or time series…input values…historical data collected during the initial monitoring period T.sub.sample, the processor is operable to generate randomly a series of possible next values at a next subsequent time interval (N+1)]);
calculate a first prediction value of the event with respect to the first initial value and calculates a second prediction value of the event with respect to the second initial value (¶¶ 0051-0055 [for each of the random number values a V value is determined using one or more of "Fractal Dimension", "Lyapunov" and/or "P&H" and these values may then be compared against the value V(S.sub.N) to identify the predicted next value x.sub.N+1 to be selected…three consecutive predicted future values… generate and output a number of predicted future data values], 0076);
calculate a deviation value from a difference between the first prediction value and the second prediction value; calculate a timing at which the deviation value becomes more than a deviation threshold value or a predetermined timing at which the deviation value becomes equal to or more than the deviation threshold value; and information regarding the predetermined timing (¶¶ 0051-0060 [consecutive predicted future values deviate from a preselected threshold value; see with 0013], 0076 [is used in predicting stock events, the monitoring period is preferably selected at least about 20 days, with individual sampling time intervals of as little as hourly or more preferably selected at daily intervals. In such embodiments the processor 12 is operable whereby: [0077] a. Using the data points one or more of, "Fractal dimension" (P&H) and "Lyapunov exponent" calculation is used to achieve a single constant that characterizes a non-linear data reference value of a fixed interval time series V(S.sub.N) for the monitored period. [0078] b. The standard deviation (sd) for the absolute value of the change in "Y" value between data points (x.sub.1 to x.sub.2, x.sub.2 to x.sub.3, x.sub.3 to x.sub.4, . . . x.sub.N) over the monitored period is determined. [0079] c. To determine the predicted data value of a next future time interval, a normal distribution curve N is defined based on the standard deviation (sd), and the curve is then centered on the data value determined at the last time interval of the time series… Each of the new non-linear data V values (V.sub.1, V.sub.2 . . . V.sub.N) are compared with the originally calculated V(S.sub.N) reference value, and the random number value having a V value that is the closest corresponding to the V(S.sub.N) value is selected, with its associated random number value chosen as the prediction for the next predicted time interval value in the time sequence. [0082] f. Using the generated time series sequence, the next subsequent predicted data value is determined by repeating steps d. to e. above. The process calculations may continue to be used to generate new predicted data values or points. Most preferably, number of new data points created in the sequence does not exceed one third of the total number of historic data points (N/3) used to achieve the constant V(S.sub.N) in step a. above. [0083] g. To create a next predicted data point from the last data point generated, go back one (or optionally N) data point and set that data point as x.sub.i. Using the set data point x.sub.i as the new first data point, the calculation is then restarted for the rapid generation of new data], 0089-0091 [ability to predict the trend of evolution of other chaotic time series is much better than those of existing methods…performances are also more stable, with a standard deviation of the error measure appearing lower than those of the other methods]).
As per claim 6, claim 6 discloses substantially similar limitations as claim 1 above; and therefore claim 6 is rejected under the same rationale and reasoning as presented above for claim 1.
As per claim 2, Gras discloses the timing calculation device according to claim 1. wherein the program instructions further cause the processor to formulate a relationship between a change in the first initial value and the second initial value and elapsed time-related information indicating an elapsed time into future or indicating information related to the elapsed time, and output information of a result obtained by the formulation (see citations above and also see ¶¶ 0051-0060 [consecutive predicted future values deviate from a preselected threshold value; see with 0013], 0076 [is used in predicting stock events, the monitoring period is preferably selected at least about 20 days, with individual sampling time intervals of as little as hourly or more preferably selected at daily intervals. In such embodiments the processor 12 is operable whereby: [0077] a. Using the data points one or more of, "Fractal dimension" (P&H) and "Lyapunov exponent" calculation is used to achieve a single constant that characterizes a non-linear data reference value of a fixed interval time series V(S.sub.N) for the monitored period. [0078] b. The standard deviation (sd) for the absolute value of the change in "Y" value between data points (x.sub.1 to x.sub.2, x.sub.2 to x.sub.3, x.sub.3 to x.sub.4, . . . x.sub.N) over the monitored period is determined. [0079] c. To determine the predicted data value of a next future time interval, a normal distribution curve N is defined based on the standard deviation (sd), and the curve is then centered on the data value determined at the last time interval of the time series… Each of the new non-linear data V values (V.sub.1, V.sub.2 . . . V.sub.N) are compared with the originally calculated V(S.sub.N) reference value, and the random number value having a V value that is the closest corresponding to the V(S.sub.N) value is selected, with its associated random number value chosen as the prediction for the next predicted time interval value in the time sequence. [0082] f. Using the generated time series sequence, the next subsequent predicted data value is determined by repeating steps d. to e. above. The process calculations may continue to be used to generate new predicted data values or points. Most preferably, number of new data points created in the sequence does not exceed one third of the total number of historic data points (N/3) used to achieve the constant V(S.sub.N) in step a. above. [0083] g. To create a next predicted data point from the last data point generated, go back one (or optionally N) data point and set that data point as x.sub.i. Using the set data point x.sub.i as the new first data point, the calculation is then restarted for the rapid generation of new data], 0089-0091 [ability to predict the trend of evolution of other chaotic time series is much better than those of existing methods…performances are also more stable, with a standard deviation of the error measure appearing lower than those of the other methods]).
As per claim 3, Gras discloses the timing calculation device according to claim 1, wherein the program instructions further cause the processor to calculate the first prediction value and the second prediction value by using a prediction model that receives an input of the initial value and outputs the prediction value (see citations above and also see ¶¶ 0051-0060 [consecutive predicted future values deviate from a preselected threshold value; see with 0013], 0076 [is used in predicting stock events, the monitoring period is preferably selected at least about 20 days, with individual sampling time intervals of as little as hourly or more preferably selected at daily intervals. In such embodiments the processor 12 is operable whereby: [0077] a. Using the data points one or more of, "Fractal dimension" (P&H) and "Lyapunov exponent" calculation is used to achieve a single constant that characterizes a non-linear data reference value of a fixed interval time series V(S.sub.N) for the monitored period. [0078] b. The standard deviation (sd) for the absolute value of the change in "Y" value between data points (x.sub.1 to x.sub.2, x.sub.2 to x.sub.3, x.sub.3 to x.sub.4, . . . x.sub.N) over the monitored period is determined. [0079] c. To determine the predicted data value of a next future time interval, a normal distribution curve N is defined based on the standard deviation (sd), and the curve is then centered on the data value determined at the last time interval of the time series… Each of the new non-linear data V values (V.sub.1, V.sub.2 . . . V.sub.N) are compared with the originally calculated V(S.sub.N) reference value, and the random number value having a V value that is the closest corresponding to the V(S.sub.N) value is selected, with its associated random number value chosen as the prediction for the next predicted time interval value in the time sequence. [0082] f. Using the generated time series sequence, the next subsequent predicted data value is determined by repeating steps d. to e. above. The process calculations may continue to be used to generate new predicted data values or points. Most preferably, number of new data points created in the sequence does not exceed one third of the total number of historic data points (N/3) used to achieve the constant V(S.sub.N) in step a. above. [0083] g. To create a next predicted data point from the last data point generated, go back one (or optionally N) data point and set that data point as x.sub.i. Using the set data point x.sub.i as the new first data point, the calculation is then restarted for the rapid generation of new data], 0089-0091 [ability to predict the trend of evolution of other chaotic time series is much better than those of existing methods…performances are also more stable, with a standard deviation of the error measure appearing lower than those of the other methods]).
As per claim 4, Gras discloses the timing calculation device according to claim 3, wherein the prediction model is a machine learning model based on a neural network (see citations above for claims 1-3 and also see ¶¶ 0070 [learning…simulator…using neural networks…MLP (multilayer perceptron – (machine learning)) model [Filippo Neri: Learning and Predicting Financial Time Series by Combining Natural Computation and Agent Simulation], 0007).
As per claim 5, Gras discloses the timing calculation device according to claim 1, wherein the event is weather prediction or stock price prediction (¶¶ 0002 [performing predictive data modeling, and more particularly a system for achieving the predictive chaos analysis of non-linear data or events, such as stock and financial market performance], 0006 [weather forecasting], 0010 [predictive models…prediction…economic and/or stock market trends and events; the prediction of seismological or meteorological events or outcomes], 0012, 0091).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Gras et al., (US 2015/0088719) in view of Phillips et al., (US 8,935,198).
As per claim 7, Gras discloses a storage medium/memory having stored therein a program for causing a computer to execute the method according to claim 6 (see citations above for claim 1 and also see ¶¶ 0013-0017, 0019-0021, 0025-0027, 0047, 0052, 0061). However, Gras does not explicitly state/show non-transitory computer-readable recording medium being used.
Analogous art Phillips discloses non-transitory computer-readable recording medium (col. 63, lines 3-32 [machine-readable…non-transitory…media on which are stored software or firmware program instructions (i.e., computer-executable process instructions)]; claim 1 of Phillips [non-transitory computer-readable medium storing computer-executable process steps]).
Therefore, it would be obvious to one of ordinary skill in the art to include in the system/method of Gras non-transitory computer-readable recording medium as taught by analogous art Phillips in order to efficiently store instructions for efficiently performing the functionality of the claimed concept since doing so could be performed readily by any person of ordinary skill in the art, with neither undue experimentation, nor risk of unexpected results (KSR-G/TSM); and also since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts of Phillips would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such concepts and features into similar systems (KSR-D); and also since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable (KSR-A). (MPEP 2141; and also see (1) 2007 Examination Guidelines for Determining Obviousness Under 35 U.S.C. 103 in View of the Supreme Court Decision in KSR International Co. v. Teleflex Inc. - Federal Register, Vol. 72, No. 195, October 10, 2007, pages 57526-57535; (2) 2010 Examination Guidelines Updated Developments in the Obviousness Inquiry After KSR v. Teleflex. -Federal Register, Vol. 75, No. 169, September 01, 2010, pages 53643-53660; and (3) materials posted at https://www.uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidelines-training-materials-view-ksr).
Conclusion
The prior art made of record on the PTO-892 and not relied upon is considered pertinent to applicant's disclosure. For example, some of the pertinent art is as follows:
Abu et al., (US 2020/0175439): Discusses a mathematical model that is emulated to predict the states of instability that can occur within the operation of the complex. Dynamic complexity of a service is demonstrated where there is an observed effect where the cause can be multiple and seemingly inter-related effects of a many-to-one or many-to-many relationship. Having assessed the dynamic complexity efficiency and the operational risk index of a service (e.g., a business, process or information technology), these indexes can be employed to emulate all attributes of a service, thereby determining how a service responds in multiple states of operation, the states where the dynamic complexity of a service can occur, optimal dynamic complexity efficiency of a service, and the singularities wherein a service becomes unstable.
Heda et al., (US 2016/0034615): Systems and methods for forecasting a time series data are disclosed. The methods include receiving a historical time-series data including a series data and a non-stationary series data. The historical time-series data is processed to obtain a unified time series data. On the unified time series data, a data distribution is plotted and the data distribution is validated based upon a rate function associated with a Large Deviation Theory (LDT). The unified time series data is split validated into vectors based on autocorrelation function (ACF). The unified time series data is further validated. A mixture of Gaussian distribution models is applied and weights are assigned to each of the Gaussian distribution model. By controlling the weights based upon various what-if scenarios, a resultant Gaussian time series data is generated. The resultant Gaussian time series data indicates forecasted time series data of the historical time series data.
Bishop et al., (US 2014/0068353): Relates to all technical fields in which one attempts to predict the variance of a quantity and direct measurements of this variance are unavailable, thus making the instantaneous variance "hidden." Such technical areas include ensemble-based state estimation in which one uses prediction of flow dependent error variances to optimize state estimation. Ensemble based state estimation is used across a very broad range of fields, including atmospheric and oceanic state estimation, oil and gas reservoir state estimation and stock price volatility prediction.
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/Gurkanwaljit Singh/
Primary Examiner, Art Unit 3625