DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This final action is responsive to the amendments filed on 11/26/25.
Claims 1-18 are pending.
Response to Arguments
With respect to the 101 rejection, the applicant argues that amended claim 1 does not recite a judicial exception because of the “performing a drilling operation….and performing the drilling operation….” (Arguments, p. 8). Applicant “asserts that a drilling operation is neither a mental process nor a mathematical concept and, therefore, amended independent claim 1 is not directed to an abstract idea.” Upon further consideration, the examiner respectfully disagrees. Application to a technological field of drilling is merely limiting that abstract idea to the specific technological field and does not make it patentable. Further, performing the drilling operation based on the output inference is merely an attempt to apply the “output inference” determination as a result or outcome, without specifically describing how the noise is accounted for by the output inference to perform the drilling operation. In addition, basing the drilling operation on “noise data” is insignificant extra-solution activity of mere data gathering (e.g., collecting data as input for an inequation, In re Grams).
Further, the applicant argues that the “link the output reference to the drilling operation in a manner that goes beyond insignificant extra-solution activity.” Upon further consideration, the examiner respectfully disagrees. Without limiting the noise-based determination to a particular algorithm, simply limiting the analysis to drilling does not make it eligible (i.e., field of use – see, e.g., Parker v. Flook).
Further, the applicant argues that application to drilling operations provides an improvement in the technical field of drilling operations. However, upon further consideration, the examiner respectfully disagrees. As explained above, merely limiting the inference and nose-based analysis does not make the eligible by way of a “field of use” restriction.
With respect to the 103 rejection, the applicant argues that the cited art fails to teach the amended “drilling operation” (Arguments, p. 9). Upon further consideration, a new ground of rejection is applied, explained 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.
Claim 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Under the broadest reasonable interpretation, the terms of the claim are presumed to have their plain meaning consistent with the specification as it would be interpreted by one of ordinary skill in the art. See MPEP 2111.
Regarding exemplary independent claim 1
Step 1 -- whether the claim falls within any statutory category. See MPEP 2106.03
Claim 1, at least, is drawn to a method (e.g., “generating”) claim. Therefore, each of these claims falls under one of the four categories of statutory subject matter (process/method, machine/product/apparatus, manufacture, or composition of matter).
Step 2A Prong One:
This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim. The “generating” and “identifying” limitations fall within the mental process grouping of abstract ideas because, e.g., “generating” is a mental process of observation and evaluation based on the observation of select data (e.g., “data”) and judgments/evaluations corresponding with, e.g., “generating a map” and “identifying a convergence in the trajectories of the outputs for the one or more empirical formulas” and, thus, related to a human mentally performing observations (of generic data) and evaluations. Mental process groupings of abstract ideas cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04.
Further, the recited “empirical formulas,” “trajectories of outputs for the one or more empirical formulas,” “based on a change to an influencer variable of the one or more empirical formulas,” “convergence in the trajectories of the outputs for the one or more empirical formulas” are merely mathematical algorithms.
“Unless it is clear that a claim recites distinct exceptions, such as a law of nature and an abstract idea, care should be taken not to parse the claim into multiple exceptions, particularly in claims involving abstract ideas.” MPEP 2106.04, subsection II.B. However, if possible, the examiner Under its broadest reasonable interpretation when read in light of the specification, these limitations encompass mental observations or evaluations that are practically performed in the human mind. See MPEP 2106.04(a)(2), subsection III. should consider the limitations together as a single abstract idea rather than as a plurality of separate abstract ideas to be analyzed individually. “For example, in a claim that includes a series of steps that recite mental steps as well as a mathematical calculation, an examiner should identify the claim as reciting both a mental process and a mathematical concept for Step 2A, Prong One to make the analysis clear on the record.” MPEP 2106.04, subsection II.B. Under such circumstances, however, the Supreme Court has treated such claims in the same manner as claims reciting a single judicial exception. Id. (discussing Bilski v. Kappos, 561 U.S. 593 (2010)). Here, the aforementioned limitations falling within the mental process grouping of abstract ideas and the limitations falling within the mathematical concepts grouping of abstract ideas; these limitations are considered together as a single abstract idea for further analysis
Step 2A, Prong Two:
This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
Regarding independent claim 1, this claim recites additional element “outputting the inference based on the convergence in the trajectories of the outputs for the one or more empirical formulas.” However, this limitation is merely insignificant extra-solution activity of data gathering (input and/or output) such as determined by CyberSource v. Retail Decisions, Inc. (transmitting information over the internet).
Further, claim 10 recites a “memory” and “processor” and claim 19 recites a “computer-readable storage medium” which provide nothing more than mere instructions to implement an abstract idea on a generic computer and/or application within a particular technological field. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. The recited computer elements are recited at a high level of generality; the computer is used as a tool to perform the generic computer function of, e.g., “generate a map.” See MPEP 2106.05(f) and the computer is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer (see, e.g., “processor”). See MPEP 2106.05(f).
Further, application to a technological field of drilling is merely limiting that abstract idea to the specific technological field and does not make it patentable. Performing the drilling operation based on the output inference is merely an attempt to apply the “output inference” determination as a result or outcome, without specifically describing how the noise is accounted for by the output inference to perform the drilling operation. In addition, basing the drilling operation on “noise data” is insignificant extra-solution activity of mere data gathering (e.g., collecting data as input for an inequation, In re Grams). Without limiting the noise-based determination to a particular algorithm, simply limiting the analysis to drilling does not make it eligible (i.e., field of use – see, e.g., Parker v. Flook).
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application, and the claim is directed to the judicial exception.
Step 2B:
This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
As explained with respect to Step 2A, Prong Two, there are several additional elements. The additional element of using the deep learning model is at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f) and associated limitations are at most results of the “apply” limitation, such as with application to generic computer elements. Taking into account whether or not the extra-solution activity is well understood, routine, and conventional in the field (See MPEP 2106.05(g)), it is noted that the use of the recited generic computer elements is/are merely an application to a technological field in an effort to apply the exception, further using a generic computer component.
In addition, and as explained above, the “outputting” limitation is merely insignificant extra-solution activity of data gathering, such as obtaining information over the Internet (CyberSource v. Retail Decisions, Inc.) and, even further, selecting a particular data source such as selecting information based on types of information (Electric Power Group, LLC v. Alstom S.A.).
Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer / in a technological field or insignificant extra-solution activity, which do not provide an inventive concept.
Independent claim 10 is rejected based on a similar rationale, with some elements of the dependent claims (mentioned below) incorporated.
Regarding dependent claims 2-9, and 11-18
The dependent claims merely narrow the previously cited abstract idea limitations. For the reasons described above with respect to independent claims, these judicial exceptions are not meaningfully integrated into a practical application, or significantly more than the abstract ideas. The claims disclose similar limitations described for the independent claims above and do not provide anything more than the mental processes and/or math that are practically capable of being performed in the human mind with the assistance of pen and paper. Therefore, the dependent claims also recite abstract ideas that do not integrate into a practical application or amount to significantly more than the judicial exception, and are rejected under U.S.C. § 101. See MPEP 2106.03.
Regarding claim 2, this claim recites the mental mapping (i.e., evaluation) of an output of the outputs based on an observed change to the influencer variable and, further, involving math of a “trajectory” of the output having a “degree of deflection.”
Claim 3 recites the mental performance of output mapping.
Claim 4 recites the mental performance of selecting observed “influencer variable” based on an evaluation of “having a highest resolution…”
Claim 5 recites the mathematical algorithm of “convergence or 5 percent or less” further involving a mere mental evaluation of a threshold.
Claim 6 recites the mental observation of the “situational bias” and, even further, basing the situational bias merely on the insignificant extra-solution activity of association based on a particular data type (e.g., “situational coordinates”) – see Electric Power Group – selecting information based on types.
Claim 7 recites the insignificant extra-solution activity of a particular data type (i.e., the “influencer variable is associated with a corresponding change…”) similar to Intellectual Ventures I LLC v. Erie Indem. Co. – limiting a database index to particular XML tags.
Claim 8 recites the math of generating a “relevance score” and further the mental observation of “identifying” the “convergence in the trajectories of the outputs.”
Claim 9 recites the mental processes of “grouping” and “measuring” each involving the performance of observation and evaluation mental processes.
The remaining dependent claims are rejected based on a similar rationale.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Collins (US 20230245239) in view of Okawa et al. (US 20220274255, Hereien “Okawa”) in view of Holtz (US 20170235007).
Regarding claim 1, Collins teaches A method of outputting an inference (output empirical formula (abstract); and Okawa makes clear generating an inference model [0067] and [0073]), comprising:
generating a map associated with situational bias in one or more empirical formulas (mapping claim variables [0003]; Examiner’s note: Okawa, below, makes clear “bias” corresponding with reducing error with respect to, e.g., first and second models, explained below), the map corresponding to trajectories of outputs for the one or more empirical formulas (percent impacts corresponding to each variable (fig. 3)), each trajectory of the trajectories based on a change to an influencer variable of the one or more empirical formulas (change with respect to a variable across time periods [0004]), the influencer variable associated with data that is stable during the change to the influencer variable (e.g., consistent time periods for the respective analysis [0004]);
identifying a convergence in the trajectories of the outputs for the one or more empirical formulas (self-correct until the predicted severity data converges within a tolerance threshold [0046]), the convergence being based on adaptable boundary conditions (e.g., convergence based on a threshold [0046]) and indicative of a compensation for the situational bias in the one or more empirical formulas (self-correcting based on a bias based on the predicted severity data biased with respect to the actual severity data [0046]); and
outputting the inference based on the convergence in the trajectories of the outputs for the one or more empirical formulas (output, e.g., the expected item damage severity output for given timer intervals [0047], figs. 4).
However, Collins fails to specifically teach adaptable boundary conditions.
Yet, in a related art, Okawa discloses boundary value obtained as selected by, e.g., an operator or selected from a point near a particular outcome value [0018], as such, it is possible to perform determining convergence between, e.g., two equations [0156[ and [0194], [0199], [0215].
It would have been obvious to one of ordinary skill in the art prior to the invention’s effective filing date to combine the boundary value of Okawa with the convergence determination of Collins to have adaptable boundary conditions. The combination would allow for, according to the motivation of Okawa, performing trajectory or difference analysis in an error output between the first estimate value and the second estimate value based on a gradient of the error (abstract) such as involving influencer variable such as environmental noise measured by sensor(s) [0008] associated at a corresponding with sensor reading data obtained from, e.g., the first and second sensor systems [0012] which are stable absent noise from each sensor system [0013]), thus allowing for estimation results’ convergence to a single value [0013] and providing an ability to identify the convergence of input data to find a true value representing the coordinates of an object, such as an a setting biased by noise [0008].
Furthermore, Okawa teaches or makes clear:
generate a map associated with situational bias in one or more empirical formulas (mapping computational variables with a first estimation model and a second estimation model [0011]), the map corresponding to trajectories of outputs for the one or more empirical formulas (changes in respective outputs of a given model in addition to gradients between models, particularly with respect to changes in trajectories of any of the outputs with respect to a change in a given input computational parameter [0011]), each trajectory of the trajectories based on a change to an influencer variable of the one or more empirical formulas (adjusting computational parameters for one or more of the models [0011]), the influencer variable associated with data that is stable during the change to the influencer variable (e.g., a stable true value [0013]);
identify a convergence in the trajectories of the outputs for the one or more empirical formulas (as for the trajectories of the calculated gradient, identify a convergence to a single value [0013]), the convergence being based on adaptable boundary conditions (a boundary condition such as a known true coordinate value [0016]) and indicative of a compensation for the situational bias in the one or more empirical formulas (compensation for a bias or error [0013]); and
output the inference based on the convergence in the trajectories of the outputs for the one or more empirical formulas (outputted model based on the determined convergence of the trajectories of the gradients to zero for each of the formulas [0011] to [0016]; such as in the case of agreement or convergence between the estimated values to improve the accuracy in estimating or outputting the coordinates of an endpoint calculated using the model(s) [0013] and [0014]).
However, Collins in view of Okawa fails to specifically teach performing a drilling operation in accordance with an inference and performing the drilling operation based on the output inference, wherein the drilling operation includes noise data that is accounted for by the output inference to perform the drilling operation.
Yet, in a related art, Holtz discloses drilling noise analysis model, the drilling noise accounted for in determining output such as control signals [0022].
It would have been obvious to one of ordinary skill in the art prior to the invention’s effective filing date to combine the performing drilling operation in accordance with noise data and inference of Holtz with the model building and inference implementation of Collins in view of Okawa to have performing a drilling operation in accordance with an inference and performing the drilling operation based on the output inference, wherein the drilling operation includes noise data that is accounted for by the output inference to perform the drilling operation. The combination would allow for, according to the motivation of Holtz, adjusting drilling, processing, or telemetry options to derive an adjusted control for drilling [0022] and [0023] thus more effectively determining drilling operations such as with respect to possibly bad anomalies [0001].
Regarding claim 2, Collins in view of Okawa in view of Holtz teaches the limitations of claim 1, as above.
Furthermore, Okawa teaches The method of claim 1, further comprising mapping an output of the outputs for the one or more empirical formulas based on the change to the influencer variable (based on having obtained a change to the influencer variable (e.g., new sensing data) [0025]), the trajectory of the output having a degree of deflection for the influencer variable (calculate a gradient (i.e., trajectory) of the output between the first estimation model and the second estimation model [0024] and [0025]).
Regarding claim 3, Collins in view of Okawa in view of Holtz teaches the limitations of claims 1 and 2, as above.
Furthermore, Okawa teaches The method of claim 2, wherein the output is mapped for each influencer variable of the one or more empirical formulas (each influencer variable including, e.g., a parameter value for each of the estimation models [0024]; that is, the output of each model is mapped to each respective parameter value, allowing for calculating a gradient or deflection between the first model and the second model, further allowing for adjusting a particular parameter value for at least one of the models to further a convergence such as by reducing an error between the respective models [0024]).
Regarding claim 4, Collins in view of Okawa in view of Holtz teaches the limitations of claims 1 and 2, as above.
Furthermore, Okawa teaches The method of claim 2, further comprising selecting, for each of the one or more empirical formulas, the influencer variable having a highest resolution for outputting the inference, wherein the degree of deflection associated with the influencer variable of the highest resolution is within a threshold range of deflection (select parameters until the update is less than a threshold; that is, a parameter is selected until the resolution of its reward no longer leads to an improvement in the model (i.e., is no longer satisfied based on the corresponding change) [0215] and [0216]; that is, the degree of deflection associated with a change in the predicted output is within a threshold range of improvement or deflection to the predicted output).
Regarding claim 5, Collins in view of Okawa in view of Holtz teaches the limitations of claims 1, 2 and 4, as above.
Furthermore, Collins teaches The method of claim 4, wherein the threshold range of deflection is at least one of adaptable based on the convergence or 5 percent or less (as for 5 percent or less, the tolerance threshold is determined to be, e.g., within 5% [0046]).
As for adaptable based on the convergence, Okawa also discloses controlling the threshold according to, e.g., input from the user [0199] or the allowable error/threshold [0301] determined based on a convergence to a true value [0156].
Regarding claim 6, Collins in view of Okawa in view of Holtz teaches the limitations of claim 1, as above.
Furthermore, Okawa teaches The method of claim 1, wherein the map associated with the situational bias is based on situational coordinates that indicate the trajectories of the outputs (coordinates associated with each estimation model, the trajectories of the output of each model in addition to the gradients/trajectories between each model used in determining the bias or error with respect to each model [0011] to [0021]).
Regarding claim 7, Collins in view of Okawa in view of Holtz teaches the limitations of claim 1, as above.
Furthermore, Okawa teaches The method of claim 1, wherein the change to the influencer variable is associated with a corresponding change to the situational coordinates for an output of the outputs for the one or more empirical formulas (a change in each parameter is associated with a change in the predicted output coordinates for the output of each model [0011], thus allowing for adjusting the computational parameters until a change in an output or a change in the gradient between outputs converges, particularly with respect to a threshold, all of which correspond with coordinates of the output of the formulas [0199]).
Regarding claim 8, Collins in view of Okawa in view of Holtz teaches the limitations of claim 1, as above.
Furthermore, Okawa teaches The method of claim 1, further comprising generating a relevance score for the influencer variable of the one or more empirical formulas (a relevance score such as a reward score for each formula parameter included in the inference model [0215]), wherein identifying the convergence in the trajectories of the outputs is based on the trajectories being associated with a threshold relevance score (update computational parameters until the update is less than or equal to a threshold [0215]).
Regarding claim 9, Collins in view of Okawa in view of Holtz teaches the limitations of claim 1, as above.
Furthermore, Okawa teaches The method of claim 1, further comprising:
grouping data associated with the one or more empirical formulas into one or more situational categories (certain inference model task [0067] and [0306]; even further, data are grouped according to a specific formula such as a first estimation model and a second estimation model [0011]); and measuring a stability of the one or more situational categories based on the change to the influencer variable (measuring a stability such as repeatedly correcting the value of a model parameter until the update is less than a threshold [0215]; even further, observe a stability of the gradient between the first and second models based on a change to, e.g., a respective model parameter [0011], the stability of the gradient determined based on adjustment performed to reduce error in the outputs [0013]).
Claims 10 to 18 recite similar limitations as claims 1 to 9, with some clarification provided below.
Regarding claim 10, Collins teaches An apparatus for outputting an inference, comprising:
a memory; and at least one processor coupled to the memory and configured (computer processor [0009]) to:
The claim recites similar limitations as claim 1 – see above.
For instance, Okawa teaches:
generate a map associated with situational bias in one or more empirical formulas (mapping computational variables with a first estimation model and a second estimation model [0011]), the map corresponding to trajectories of outputs for the one or more empirical formulas (changes in respective outputs of a given model in addition to gradients between models, particularly with respect to changes in trajectories of any of the outputs with respect to a change in a given input computational parameter [0011]), each trajectory of the trajectories based on a change to an influencer variable of the one or more empirical formulas (adjusting computational parameters for one or more of the models [0011]), the influencer variable associated with data that is stable during the change to the influencer variable (e.g., a stable true value [0013]);
identify a convergence in the trajectories of the outputs for the one or more empirical formulas (as for the trajectories of the calculated gradient, identify a convergence to a single value [0013]), the convergence being based on adaptable boundary conditions (a boundary condition such as a known true coordinate value [0016]) and indicative of a compensation for the situational bias in the one or more empirical formulas (compensation for a bias or error [0013]); and
output the inference based on the convergence in the trajectories of the outputs for the one or more empirical formulas (outputted model based on the determined convergence of the trajectories of the gradients to zero for each of the formulas [0011] to [0016]).
Further, Holtz discloses drilling noise and inference (figs. 2 and 3)
Regarding claim 11, Collins in view of Okawa in view of Holtz teaches the limitations of claim 10, as above.
Furthermore, Collins teaches The apparatus of claim 10, wherein the at least one processor is further configured to map an output of the outputs for the one or more empirical formulas based on the change to the influencer variable (percent impact values that each correspond to a particular claim variable (abstract); claim variable as influencer variable [0003]), the trajectory of the output having a degree of deflection for the influencer variable (percent (i.e., degree) of impact or deflection [0005], fig. 3).
Furthermore, Okawa even discloses output gradient or deflection [0011] corresponding with a level of difference [0309].
Regarding claim 12, Collins in view of Okawa in view of Holtz teaches the limitations of claims 10 and 11, as above.
Furthermore, Okawa teaches The apparatus of claim 11, wherein the output is mapped for each influencer variable of the one or more empirical formulas (mapping input parameters to estimation outputs [0011]).
Regarding claim 13, Collins in view of Okawa in view of Holtz teaches the limitations of claims 10 and 11, as above.
Furthermore, Collins teaches The apparatus of claim 11, wherein the at least one processor is further configured to select, for each of the one or more empirical formulas, the influencer variable having a highest resolution for outputting the inference (sort up to the selected highest value from among all the output values for each claim variable [0059] to [0062]), wherein the degree of deflection associated with the influencer variable of the highest resolution is within a threshold range of deflection (sorting output and a output threshold representing outputs that are, e.g., above the threshold and sorted in order [0004]).
Regarding claim 14, Collins in view of Okawa in view of Holtz teaches the limitations of claims 10, 11 and 13, as above.
Furthermore, Collins teaches The apparatus of claim 13, wherein the threshold range of deflection is at least one of adaptable based on the convergence or 5 percent or less (5% threshold [0046]).
Regarding claim 15, Collins in view of Okawa in view of Holtz teaches the limitations of claim 10, as above.
Furthermore, Okawa teaches The apparatus of claim 10, wherein the map associated with the situational bias is based on situational coordinates that indicate the trajectories of the outputs (trajectories or gradients between outputs corresponding with coordinate values [0011]).
Regarding claim 16, Collins in view of Okawa in view of Holtz teaches the limitations of claim 10, as above.
Furthermore, Okawa teaches The apparatus of claim 10, wherein the change to the influencer variable is associated with a corresponding change to the situational coordinates for an output of the outputs for the one or more empirical formulas (corresponding change between first estimate value of coordinates and second estimate value of coordinates and corresponding influencer parameters to, e.g., cause coordinates of the endpoint to be closer to a goal value [0011]).
Regarding claim 17, Collins in view of Okawa in view of Holtz teaches the limitations of claim 10, as above.
Furthermore, Collins teaches The apparatus of claim 10, wherein the at least one processor is further configured to generate a relevance score for the influencer variable of the one or more empirical formulas (e.g., predicted severity data [0046]), wherein identifying the convergence in the trajectories of the outputs is based on the trajectories being associated with a threshold relevance score (threshold score [0046]).
Regarding claim 18, Collins in view of Okawa in view of Holtz teaches the limitations of claim 10, as above.
Furthermore, Collins teaches The apparatus of claim 10, wherein the at least one processor is further configured to:
group data associated with the one or more empirical formulas into one or more situational categories (categories such as a particular dataset (e.g., first claim dataset) in addition to a first plurality of variables from the first claim dataset and even parsing the data according to particular time periods [0003]); and
measure a stability of the one or more situational categories based on the change to the influencer variable (change in variable(s) from time period to time period [0003] such as a change for each claim variable based on the first and second time periods [0036]).
Furthermore, Okawa even discloses measuring stability based on a change to the corresponding parameter and with respect to a known, stable true value [0156] and [0013] based on a situational category corresponding with a respective task [0080].
Conclusion
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 extension fee 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 date of this final action.
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/JASON T EDWARDS/ Examiner, Art Unit 2145