DETAILED ACTION
This action is in response to the original filing on April 20, 2023 and the Remarks and Amendments filed June 30, 2026. Claims 1-20 are pending and have been considered below. Claims 1, 9, and 15 are independent claims. Claims 1, 3, 9, 11, 15, and 17-20 are amended. Claims 2, 10, and 16 are canceled.
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 .
Response to Amendment
In review of the Applicant’s amendments and remarks, filed June 30, 2026, the objections to the specification and drawings made in the previous office action have been withdrawn.
The rejections of claims 1-20 under 35 U.S.C. 101 set forth in the previous office action are withdrawn in view of the amendments made to the independent claims 1, 9, and 15.
Response to Arguments
Applicant’s arguments filed June 30, 2026 regarding the rejections from the previous office action made under 35 U.S.C. 103 have been fully considered but are not persuasive.
One page 9 of the Remarks, Applicant asserts that “the cited reference does not teach or otherwise show all elements of independent claim 1.” Referring to page 9 of the Remarks, Applicant states that to establish prima facie obviousness, there must be (1) some suggestion or motivation to modify or combine reference teachings within the prior art, (2) a reasonable expectation of success, and (3) the prior art references must teach or suggest all of the claim features. Applicant cites as reasoning “In re Royka (490 F.2d 981, CCPA 1974)” regarding how “the prior art reference(s) must teach or suggest all of the claim features,” and submits that “Raykov, in ¶59, describes selecting among different inference techniques during training; it does not teach setting the initial model parameters of that technique based on a linearized set of coefficients, as now recited in claim 1” on page 10 of the Remarks. Examiner respectfully disagrees. Raykov explicitly teaches selecting a first inference technique during model training to learn model parameters from the training data and lists a Gibbs sampling inference technique as an example of a first inference technique (¶59 “During training of the machine learning model, a first inference technique can be used to learn model parameters from the training data… For example, the first inference technique may comprise a Markov Chain Monte Carlo inference technique, Beam sampling inference technique or Gibbs sampling inference technique,” ¶129 “both beam and Gibbs sampler inference algorithms are guaranteed to converge on the optimal [infinite hidden Markov model] fit eventually,” ¶131 “The specific choice of a MCMC method is not essential here as when run to convergence, MCMC methods have guarantees to find the optimal parameterization (we also tested Beam sampling and Gibbs sampling). Once convergence is reached, we compute and store the non-collapsed infinite HMM model parameters”). Gibbs sampling, as understood in the art, is a special case of the Metropolis-Hastings algorithm. As stated in the previous office action and claim 1’s rejection below, while Raykov fails to teach that the Gibbs sampling is used to learn model parameters based on a linearized set of coefficients, Pašić specifically teaches deriving a linearized set of coefficients from non-linear sensor data (Page 42, Fig. 4 5 and “Sensor characteristic linearisation” section: “If, for example a sensor transfer function is described by v = axb then it is possible to determine a and b (for example by transforming the expression to V = bX + n…,” hence (3) all claimed features are taught or suggested by the combination of Raykov and Pašić).
Raykov’s “training data” that is used to set model parameters are implied to be a series of numerical values (Raykov, ¶37 “A passive infrared (PIR) sensor generates an output value which depends on the amount of infrared (IR) radiation that is incident on the sensor… a PIR sensor does not attribute the detected radiation to any particular position within its field of view, but simply outputs a value dependent on the amount of infrared radiation detected… PIR sensors are commonly employed in buildings for human motion detection,” ¶49 “To train the model, a number of sets of sensor values are gathered in meetings with different known numbers of occupants, so that the spread parameters gathered for each meeting (or over each time window within the same meeting) can be plotted against the known numbers of occupants in each case,” ¶96 “The recorded raw digital PIR signal comprises a stream of real numbers in the range 0 to 1 with 4 decimal place accuracy”). One of ordinary skill in the art could reasonably substitute the training data of Raykov with the linearized coefficients of Pašić, which are also implied to be real numerical values (Pašić, Pages 42-43 “If, for example a sensor transfer function is described by v = axb then it is possible to determine a and b (for example by transforming the expression to V = bX + n where V = log(v), X = log x, and n = log a and using “Least Squares”). Knowing a and b, the inverse function can be easily determined (x = 1/b√v/a) in order to linearise the sensor transfer function”) when combining the teachings of Raykov and Pašić without undue experimentation (hence (2) a reasonable expectation of success).
Finally, as explained in the previous office action and claim 1’s rejection below, both Pašić and Raykov are analogous art to the claimed invention because they are both in the same field of endeavor of processing sensor data (MPEP § 2141.01(a)(I)). The teaching, suggestion, or motivation to combine the two references is stated within Pašić (Page 2 “Digital Signal Processing (DSP) provides a powerful numeric means of extracting useful information from sensor data applied to a system… It is a method of processing real world signals (represented digitally) using mathematical techniques to perform transformations or extract information. It is best to optimize the use of all the resources available in any system. Especially it is important to make good use of DSP and software to handle problems that are more suited to those components. This refers to errors such as… non-linear characteristics of a sensor. Attempts at linearization… in the analogue domain are generally difficult. Processor power is very inexpensive and it is much easier to do sensor linearisation and calibration in software. Why use digital signal processing[3]? Flexibility – differing algorithms and coefficients may be applied at will. Operations are noise free. Algorithms not realizable with analogue components may be employed”) and Raykov (¶59 “By using different inference techniques during training and test phases, this allows the device provided in the field to use less memory and have reduced computation time, while still enabling greater predictive accuracy by using the more accurate first inference technique during training when the computational burden required is less of a factor as this can be done offline using a more powerful computer”), as explained in the rejection below. A reason why one of ordinary skill in the art would be prompted to combine the two references is implicit, since “the ‘improvement’ is technology-independent and the combination of references results in a product or process that is more desirable, for example because it is stronger, cheaper, cleaner, faster, lighter, smaller, more durable, or more efficient” (MPEP § 2143(I)(G)). For example, as stated by Pašić, processor power is inexpensive when executing linearization of digital sensor data in software, so one may combine the inexpensive digital sensor processing technique of Pašić to linearize sensor data for “ready availability of instrumentation and use in control systems” (Pašić, Page 36, ¶2) with the more computationally expensive Gibbs sampling technique of Raykov to set parameters for a model that can more accurately predict sensor values (Raykov, ¶48 “in a smart building application it may be desirable to be able to control systems such as lighting or air conditioning to adapt to the number of occupants present,” hence (1) some suggestion or motivation to modify or combine the teachings of Raykov and Pašić).
In consideration of these conclusions, the previous rejections under 35 U.S.C. 103 still stand for independent claims 1, 9, and 15, and their associated dependent claims 3-8, 11-14, and 17-20, respectively.
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 (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 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.
Claims 1, 3-7, 9, 11-13, 15, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Fotak et al. (US 20220198305 A1, hereinafter Fotak) in view of Wang (CN 108254787 A, hereinafter Wang) and further in view of Gullikson et al. (US 20230110056 A1, hereinafter Gullikson), and further in view of Pašić (“Generalised Sensor Linearisation and Calibration,” hereinafter Pašić), and further in view of Raykov et al. (US 20170364817 A1, hereinafter Raykov).
Regarding claim 1:
Fotak teaches a method (¶39 “The software framework (also called a software framework process, method, etc.)”) to detect anomalies in sensor data, the method comprising: receiving historical sensor data in non-linear form (¶39 “(A) receive (read) data (a collection of data); and (B) detect abnormal values (detect at least one anomaly) contained in (positioned in or associated with) the data. A specific and non-limiting example of the data includes time-series data (also called, historical data) … sensors and/or systems are configured to (constantly) emit (transmit) a (relentless) stream of time-series data, which is recorded (digitally recorded and stored in a server, etc.),” Fig. 1 – 1, ¶48 “the training data 1 includes (preferably) actual volumes of data (historical data received by a server) extending over (during) a training period,” Figure 1 depicts historical sensor data in non-linear form).
Regarding the limitation applying a non-iterative acceleration technique for systems of non-linear equations to the historical sensor data in non-linear form to determine a set of coefficients, Fotak teaches the historical sensor data in non-linear form (Fig. 1 – 1, ¶48 as explained above). However, Fotak fails to teach applying a non-iterative acceleration technique for systems of non-linear equations to the historical sensor data in non-linear form to determine a set of coefficients.
Wang, in the same field of endeavor, teaches applying a non-iterative acceleration technique for systems of non-linear equations to data in polynomial form to determine a set of coefficients (from Machine Translation: ¶109 “Suppose we have a polynomial, as follows:”
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¶111 “The summation of its finite terms is as follows:”
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¶113 “Then, the γth term of the polynomial obtained by equation (11) after one Shanks transformation is as follows:”
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¶115 “when m = 3 in equation (6) above, the wave propagation distance polynomial, i.e., the first threshold model obtained by equation (7) after a Shanks transformation, is specifically as follows:”
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Equation (13) depicts a set of coefficients determined for a polynomial function after applying a Shanks transformation, or a non-iterative acceleration technique for systems of non-linear equations).
Fotak fails to teach converting the historical sensor data in non-linear form to linearized data. However, Gullikson, in the same field of endeavor, teaches this limitation (Fig. 1 – 102-104, Fig. 2 – 234, Fig. 4 – 234-104, ¶84 “the historical sensor data 234 is received and preprocessed at the preprocessor 104,” ¶50 “the preprocessor 104 may also… scale or normalize values of the sensor data 102,” ¶89 “One example of nonlinear scaling includes shifting the data so that a median of the data is zero (0) and using an inverse hyperbolic sine function, which approximates a symmetric log-transform. Another example of nonlinear scaling is using a power transform, such as a box-cox transform,” wherein a “log-transform” and a “box-cox” transform are known techniques in the art to convert data in non-linear form to linearized data).
Fotak fails to teach converting the set of coefficients to a linearized set of coefficients. However, Pašić, in the same field of endeavor, teaches this limitation (Page 42 Figure 4 5 depicts linearization of “logarithmic” and “exponential” data, which are well-known nonlinear functions with implicit nonlinear coefficients, Pages 42-43 under “Sensor characteristic linearization” section: “Sensor characteristic linearisation is based on function transformation (Figure 4 5)… If, for example a sensor transfer function is described by v = axb then it is possible to determine a and b (for example by transforming the expression V = bX + n where V = log(v), X = log x and n = log a and using “Least Squares“) Knowing a and b, the inverse function can easily be determined… in order to linearise the sensor transfer function,” wherein “V = bX + n” is a function in linear form with “b” and “n” as a linearized set of coefficients).
Regarding training a Bayesian statistical model to generate a confidence range corresponding to a forecast period based on the linearized data and the linearized set of coefficients, wherein training the Bayesian statistical model includes setting initial model parameters for a Metropolis-Hastings algorithm-based technique based on the linearized set of coefficients to reduce a duration of a training period of training the Bayesian statistical model, Fotak teaches training a statistical model to generate a confidence range corresponding to a forecast period based on a collection of data (Fig. 1 depicts a “confidence interval” associated with the “forecasting period” of weeks 4 and 5, ¶40 “The framework includes computer control-logic code… to identify (find), select, and train a model… to learn patterns in the data (the collection of data),” ¶41 “The model (also called a statistical model) includes, preferably, any type of statistical model configured to forecast predicted values,” Fig. 2A-2B – 14-20, ¶101 “BLOCK 20 includes computer control-logic code configured to forecast values for a future time period (such as the next seven days, etc.), for the case where: (A) the original model (as processed by BLOCK 14) has an appropriate confidence level, or (B) a retrained model has an appropriate confidence level (as processed by BLOCK 19)”). However, Fotak fails to teach a Bayesian statistical model and the linearized data and the linearized set of coefficients, wherein training the Bayesian statistical model includes: setting initial model parameters for a Metropolis-Hastings algorithm-based technique based on the linearized set of coefficients to reduce a duration of a training period of training the Bayesian statistical model.
Gullikson teaches a Bayesian statistical model (¶27 “Examples of machine-learning models include, without limitation… Bayesian models”) and the linearized data (Fig. 1 – 102-104, Fig. 2 – 234, Fig. 4 – 234-104, ¶84, 50, and 89, as explained above). However, Gullikson fails to teach and the linearized set of coefficients, wherein training the Bayesian statistical model includes: setting initial model parameters for a Metropolis-Hastings algorithm-based technique based on the linearized set of coefficients to reduce a duration of a training period of training the Bayesian statistical model.
Pašić teaches and the linearized set of coefficients (Page 42 under “Sensor characteristic linearization” section: “V = bX + n”). However, the combination of Fotak, Gullikson, and Pašić fails to teach wherein training the Bayesian statistical model includes: setting initial model parameters for a Metropolis-Hastings algorithm-based technique based on the linearized set of coefficients to reduce a duration of a training period of training the Bayesian statistical model.
Raykov, in the same field of endeavor, teaches wherein training the model includes: setting initial model parameters for a Metropolis-Hastings algorithm-based technique based on the training data to reduce a duration of a training period of training the model (¶59 “During training of the machine learning model, a first inference technique can be used to learn model parameters from the training data… the first inference technique may comprise a Markov Chain Monte Carlo inference technique… or Gibbs sampling inference technique… By using different inference techniques during training… this allows the device provided in the field to… have reduced computation time,” wherein a “Gibbs sampling inference technique” encompasses a special case of a Metropolis-Hastings algorithm-based technique).
Fotak further teaches receiving a sensor value in the forecast period (¶39 “sensors and/or systems are configured to (constantly) emit (transmit) a (relentless) stream of time-series data, which is recorded (digitally recorded and stored in a server, etc.),” Fig. 1 – 3, ¶48 “The actual volume of data 3 (that is received by the server) extends during (into) week 4 and week 5 (that is, during a selected time span in which actual data is received)”).
Fotak further teaches comparing the sensor value to the confidence range (Fig. 1 – 2, 3, 4x, 6x, ¶48 “The framework includes computer control-logic code further configured to determine whether or not the actual volume of data 3 is within the confidence interval 2. For instance, the control-logic code may further make (identify) a determination that the actual volume of data 3 is not within the confidence interval 2 at the following example instances: (A) an instance 4 (a high volume anomaly 4×), for the case where a relatively higher volume of data is received, during a selected time span (where the actual volume of data on a given day is determined to be above the confidence interval 2); and/or (B) an instance (a low volume anomaly 6×), for the case where a relatively lower volume of data is received, during a selected time span (where the actual volume of data on a given day is determined to be below the confidence interval 2)”).
Fotak further teaches and in the event the sensor value is detected outside the confidence range, determining an anomaly (Fig. 2B – 7-8, ¶60 “BLOCK 7 includes computer control-logic code configured to… process a volume of data having time-series data, monitor the volume of data, and detect whether there is at least one anomaly in the a volume of data by determining whether an actual value falls outside of a confidence interval to be generated… Detecting whether said at least one anomaly was detected includes, preferably, determining whether an actual value falls outside of a confidence interval,” ¶62 “BLOCK 8 includes computer control-logic code configured to (A) label (identify) the anomaly that was detected, for the case where said at least one anomaly was detected (as determined in BLOCK 7)”).
Fotak, Wang, Gullikson, Pašić, and Raykov are analogous to the claimed invention as all are from the same field of endeavor of processing data received from sensors. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the Bayesian statistical model and linearization of data of Gullikson, the non-iterative acceleration technique for systems of non-linear equations of Wang, the linearized set of coefficients of Pašić, and the setting initial model parameters for a Metropolis-Hastings algorithm-based technique of Raykov with the methodology of Fotak. The motivation to do so is to design systems and methods that “can provide cost-beneficial anomaly detection for relatively large numbers of assets that are not identical” (Gullikson, ¶20), to provide faster and more accurate data models (Wang, from Machine Translation: ¶135 “the first pre-set model… obtained by the Shanks transformation have a lower order than the original calculation formula, and have higher accuracy and convergence speed… so that the travel time calculation can meet… high accuracy and speed requirements”), to design signal processing software that is inexpensive, flexible, and noise free (Pašić, Page 2 ¶4 “Processor power is very inexpensive and it is often much easier to do sensor linearisation and calibration in software” and under “Why use digital signal processing[3]?”), and to design a training method that can “use less memory and have reduced computation time, while still enabling greater predictive accuracy” (Raykov, ¶59).
Regarding claim 3, Fotak in view of Wang and further in view of Gullikson, and further in view of Pašić, and further in view of Raykov teaches the method of claim 1 (and thus the rejection of claim 1 is incorporated).
Fotak fails to teach wherein the Metropolis-Hastings algorithm-based technique includes a Gibbs sampler. However, Raykov teaches this limitation (¶59 “a Markov Chain Monte Carlo inference technique… or Gibbs sampling inference technique”).
Fotak and Raykov are analogous to the claimed invention as all are from the same field of endeavor of processing data received from sensors. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the Gibbs sampler of Raykov with the methodology of Fotak. The motivation to do so is to design a training method that can “use less memory and have reduced computation time, while still enabling greater predictive accuracy” (Raykov, ¶59).
Regarding claim 4, Fotak in view of Wang and further in view of Gullikson, and further in view of Pašić, and further in view of Raykov teaches the method of claim 1 (and thus the rejection of claim 1 is incorporated).
Fotak fails to teach wherein the non-iterative acceleration technique for systems of non-linear equations includes a Shank transformation and the set of coefficients include Shanks transformation coefficients. However, Wang teaches this limitation (¶109-115 and Equations (6), (7), and (13) as depicted above with respect to claim 1; the coefficients for a polynomial function derived after a Shank transformation as depicted in Equation (13) encompass Shanks transformation coefficients).
Fotak and Wang are analogous to the claimed invention as all are from the same field of endeavor of processing data received from sensors. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the Shanks transformation and Shanks transformation coefficients of Wang with the methodology of Fotak. The motivation to do so is to provide faster and more accurate data models (Wang, from Machine Translation: ¶135 “the first pre-set model… obtained by the Shanks transformation have a lower order than the original calculation formula, and have higher accuracy and convergence speed… so that the travel time calculation can meet… high accuracy and speed requirements”).
Regarding claim 5, Fotak in view of Wang and further in view of Gullikson, and further in view of Pašić, and further in view of Raykov teaches the method of claim 1 (and thus the rejection of claim 1 is incorporated).
Fotak further teaches wherein the confidence range is time dependent (Fig. 2B – 18, Fig. 3, ¶87 “BLOCK 18 includes computer control-logic code configured to determine appropriateness by first finding (identifying) a distance between the 80th percentile and the 20th percentile from the volume of data over a predefined time period (such as the last two months). The distance between the 80th percentile and the 20th percentile is called the 80/20 distance (reference is made to FIG. 3 for an embodiment of the 80/20 distance). Referring back to FIG. 2, BLOCK 18 includes computer control-logic code configured to label the confidence level as APPROPRIATE for use, for the case where all of the confidence interval widths (that were generated) is smaller than the 80/20 distance,” wherein “confidence interval widths” that are generated based on “the volume of data over a predefined time period” encompass wherein the confidence range is time dependent).
Regarding claim 6, Fotak in view of Wang and further in view of Gullikson, and further in view of Pašić, and further in view of Raykov teaches the method of claim 1 (and thus the rejection of claim 1 is incorporated).
Fotak further teaches wherein the forecast period is greater than 10 seconds (Fig. 1, ¶46 “The framework includes computer control-logic code further configured to… use the model to forecast volumes of data (to be received by the server, such as over the next two weeks, etc.),” Figure 1 depicts a “forecasting period” of two weeks).
Regarding claim 7, Fotak in view of Wang and further in view of Gullikson, and further in view of Pašić, and further in view of Raykov teaches the method of claim 1 (and thus the rejection of claim 1 is incorporated).
Fotak further teaches wherein the forecast period is equal to or greater than 2 minutes (Fig. 1, ¶46 as explained above with respect to claim 6).
Regarding claim 9:
Fotak teaches a system comprising: one or more processors of a machine (¶12 “there is provided a method to be carried out by a processor of a computing platform”)
Fotak further teaches and a memory storing instructions that, when executed by the one or more processors, cause the machine to perform operations comprising (¶12 “a memory assembly having encoded thereon executable control-logic instructions configured to be executable by the processor, and also configured to urge the processor to carry out the method”): receiving historical sensor data in non-linear form (¶39, Fig. 1 – 1, ¶48, all as explained above with respect to claim 1).
Claims 9 and 11-13 recite a system that parallels the method claims of 1, 3-4, and 6, respectively. Therefore, the analysis discussed above with respect to claims 1, 3-4, and 6 also applies to claims 9 and 11-13, respectively. Accordingly, claims 9 and 11-13 are rejected based on substantially the same rationale as set forth above with respect to claims 1, 3-4, and 6, respectively.
Claims 15 and 17-19 recite a machine readable storage medium that parallels the system claims of 9 and 11-13, respectively. Therefore, the analysis discussed above with respect to claims 9 and 11-13 also applies to claims 15 and 17-19, respectively. Accordingly, claims 15 and 17-19 are rejected based on substantially the same rationale as set forth above with respect to claims 9 and 11-13, respectively.
Claim 8, 14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Fotak in view of Wang and further in view of Gullikson, and further in view of Pašić, and further in view of Raykov, and further in view of Silberman et al. (US 20220116052 A1, hereinafter Silberman).
Regarding claim 8, Fotak in view of Wang and further in view of Gullikson, and further in view of Pašić, and further in view of Raykov teaches the method of claim 1 (and thus the rejection of claim 1 is incorporated).
Fotak fails to teach retrieving historical sensor data in substantially linear form. However, Silberman, in the same field of endeavor, teaches this limitation (Fig. 1 – 110, 135, 155, ¶75 “The vehicle computing system 110 can be configured to obtain sensor data 155 generated by one or more sensors [135] of the autonomous vehicle 105,” Fig. 2 – 206, ¶74 “The sensor data 155 can be compressed, at (204)… and then stored, at (206),” wherein “sensor data” that is “stored” in “Memory,” as depicted in Figure 2, encompasses historical sensor data, ¶77 “the raw image data can be generated (e.g., by the camera) in a linear color space”).
Silberman further teaches and converting the historical sensor data in substantially linear form to generate the historical sensor data in non-linear form (Fig. 1 – 110, 135, ¶77 “before applying the lossy compression to the sensor data 155, at (206), the vehicle computing system 110 can convert the sensor data 155 into a non-linear space (e.g., from a linear space). For instance, the raw image data can be generated (e.g., by the camera) in a linear color space and converted into a non-linear color space”).
Fotak and Silberman are analogous to the claimed invention as all are from the same field of endeavor of processing data received from sensors. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the linear data and the converting of linear data to non-linear form of Silberman with the methodology of Fotak. The motivation to do so is to reduce noise from sensor data and improve sensor data quality (Silberman, ¶77 “Storing image data in a non-linear color space can provide several advantages… This allocation or mapping reduces and/or removes noise from the images, thereby improving image quality”).
Claim 14 recites a system that parallels the method claim of 8. Therefore, the analysis discussed above with respect to claim 8 also applies to claim 14. Accordingly, claim 14 is rejected based on substantially the same rationale as set forth above with respect to claim 8.
Claim 20 recites a machine readable storage medium that parallels the method claim of 8. Therefore, the analysis discussed above with respect to claim 8 also applies to claim 20. Accordingly, claim 20 is rejected based on substantially the same rationale as set forth above with respect to claim 8.
Conclusion
THIS ACTION IS MADE FINAL. 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.
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/WILLIAM M LEE/
Examiner, Art Unit 2145
/CESAR B PAULA/Supervisory Patent Examiner, Art Unit 2145
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