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 .
Response to Amendment
Applicant’s Remarks, filed September 25th, 2025, has been fully considered and entered. Accordingly, claims 1-3, and 5-20 are pending in the case. Claims 1-3, 5, 7-9, and 12-20 were amended. Claims 21-25 were cancelled. Claims 1, 11, 12, and 16 are the independent claims.
In light of Applicant’s Amendment, the Claim Objection of claim 5 has been withdrawn.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-3, and 5-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1,
At Step 1:
The claim is directed to a “method” and thus directed to a statutory category.
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
“setting first input data and the first target condition specifying a target value of an output of an objective function,” as drafted this recites a mentally performable process as an evaluation or judgement. One can mentally evaluate or judge setting data.
“generating, using a first function that models the objective function, a predicted first output data corresponding to the first input data and relative to the target value,” as drafted this recites a mentally performable process as an evaluation or judgement. One can mentally evaluate or judge generating data based on a function.
“generating, using a second function that provides a result of comparison between the predicted first output data and the first target condition, second input data that derives, using the first function, an output data closer to the target value compared to the predicted first output data,” as drafted this recites a mentally performable process as an evaluation or judgement. One can mentally evaluate or judge generating data using a function.
“wherein the second function comprises a first component and a second component, where the first component corresponds to a difference between a mean value of the predicted first output data and the target value and the second component corresponds to a standard deviation value of the predicted first output data,” as drafted this recites a mentally performable process as an evaluation or judgement. One can mentally evaluate or judge performing statistical analysis.
At Step 2A, Prong Two:
The claim recites the following additional elements:
“providing a user interface comprising a fourth section configured to receive a third user input of setting a first target condition,” which is a high-level recitation of a generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
Looking at the claim as a whole does not change this conclusion and the claim is ineligible.
Regarding claim 2,
At Step 1:
The claim is directed to a “method” and thus directed to a statutory category.
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
“wherein the first target condition comprises a first target value,” as drafted this recites a mentally performable process as an evaluation or judgement. One can mentally evaluate or judge setting data.
At Step 2A, Prong Two:
The claim does not recite any additional elements.
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
Looking at the claim as a whole does not change this conclusion and the claim is ineligible.
Regarding claim 3,
At Step 1:
The claim is directed to a “method” and thus directed to a statutory category.
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
“wherein the first target condition comprises a first target range, and the generating of the second input data comprises generating, to be the second input data, input data that derives, using the first function, output data within the first target range in response to the predicted first output data not being within the first target range,” as drafted this recites a mentally performable process as an evaluation or judgement. One can mentally evaluate or judge performing statistical analysis.
At Step 2A, Prong Two:
The claim does not recite any additional elements.
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
Looking at the claim as a whole does not change this conclusion and the claim is ineligible.
Regarding claim 5,
At Step 1:
The claim is directed to a “method” and thus directed to a statutory category.
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
“wherein the generating of the second input data comprises generating the second input data by applying different weights to the first component and the second component,” as drafted this recites a mentally performable process as an evaluation or judgement. One can mentally evaluate or judge performing statistical analysis.
At Step 2A, Prong Two:
The claim does not recite any additional elements.
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
Looking at the claim as a whole does not change this conclusion and the claim is ineligible.
Regarding claim 6,
At Step 1:
The claim is directed to a “method” and thus directed to a statutory category.
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
“wherein input data is repetitively determined through gradual target conditions comprising the first target condition,” as drafted this recites a mentally performable process as an evaluation or judgement. One can mentally evaluate or judge performing statistical analysis.
At Step 2A, Prong Two:
The claim does not recite any additional elements.
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
Looking at the claim as a whole does not change this conclusion and the claim is ineligible.
Regarding claim 7,
At Step 1:
The claim is directed to a “method” and thus directed to a statutory category.
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
“setting a second target condition; generating predicted second output data corresponding to the second input data using the first function; and generating third input data using the second function,” as drafted this recites a mentally performable process as an evaluation or judgement. One can mentally evaluate or judge performing statistical analysis.
At Step 2A, Prong Two:
The claim does not recite any additional elements.
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
Looking at the claim as a whole does not change this conclusion and the claim is ineligible.
Regarding claim 8,
At Step 1:
The claim is directed to a “method” and thus directed to a statutory category.
At Step 2A, Prong One:
The claim further recites limitations corresponding to the judicial exception recited in parent claim 1.
At Step 2A, Prong Two:
The claim recites the following additional elements:
“providing a user interface, wherein the user interface comprises: a first section configured to display a plurality of points of reference (PORs) corresponding to different input data and to receive a first user input of selecting a first POR corresponding to the first input data among the plurality of PORs; a second section configured to display the first input data corresponding to the first user input and to receive a second user input of modifying the first input data; a third section configured to display the predicted first output data based on the first function; the fourth section configured to display a settable condition and to receive a third user input of setting the first target condition; and a fifth section configured to display recommended input data comprising the second input data based on the second function,” which is a high-level recitation of a generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
Looking at the claim as a whole does not change this conclusion and the claim is ineligible.
Regarding claim 9,
At Step 1:
The claim is directed to a “method” and thus directed to a statutory category.
At Step 2A, Prong One:
The claim further recites limitations corresponding to the judicial exception recited in parent claim 8.
At Step 2A, Prong Two:
The claim recites the following additional elements:
“wherein the third section comprises a first graph representing the predicted first output data according to the first input data, and in response to the first input data being modified according to the second user input, the first output data is changed based on the first function, and the first graph is updated based on the modified first input data and the changed first output data,” which is a high-level recitation of a generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
Looking at the claim as a whole does not change this conclusion and the claim is ineligible.
Regarding claim 10,
At Step 1:
The claim is directed to a “method” and thus directed to a statutory category.
At Step 2A, Prong One:
The claim further recites limitations corresponding to the judicial exception recited in parent claim 8.
At Step 2A, Prong Two:
The claim recites the following additional elements:
“wherein the fifth section comprises a second graph representing a degree of target achievement and uncertainty of each recommended input data, and in response to recommended input data corresponding to the second input data being selected from the second graph, the second section and the third section are updated based on the second input data,” which is a high-level recitation of a generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
Looking at the claim as a whole does not change this conclusion and the claim is ineligible.
Independent claims 11, 12, and 16 and dependent claims 13-15, and 17-20 correspond to and recite the same judicial exception as claims 1-3, and 5-10, except that in claim 11 at Step 1, the claim is directed to a “non-transitory computer-readable storage medium” and thus directed to the statutory category of machine, and in claims 12 and 16 at Step 1, the claims are directed to an “apparatus” and thus directed to the statutory category of machine. Therefore, claims 11-20 are rejected accordingly.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, and 5-20 are rejected under 35 U.S.C. 103 as being unpatentable over Yue et al. (User Interfaces for High-Dimensional Design Problems) in view of Held et al. (US 2023/0259079 A1).
Regarding claim 1, Yue teaches a processor-implemented method with data exploration for optimizing of a design and process development of a semiconductive product, comprising:
providing a user interface comprising a fourth section configured to receive a third user input of setting a first target condition (see Yue, Pages 16, 19, Figures 4, 6, “Fig. 4 Concept of the sequential line search method” [Figure 4 shows an interactive interface that includes multiple inputs]),
setting first input data and the first target condition specifying a target value of an output of an objective function (see Yue, Pages 3, 5, 9-13, “We focus on the development of effective user interfaces for such high-dimensional design problems, where, mathematically, the user has control of a set of parameters 𝒛 = (𝑧1, . . . , 𝑧𝑑 )⊤ over a design space Z ⊂ R 𝑑 to generate the output 𝒙 = 𝒇 (𝒛) of the design, with the quality of each design judged by the user’s own goodness function 𝑔(𝒙) = 𝑔(𝒇 (𝒛)), or 𝑔(𝒛) for short. … This is because the goodness function, in our case, the objective function 𝑔 is only indirectly defined using perceptual preference, and even the user does not know what the function looks like.” [The user has control of a set of parameters (i.e., setting first input data).]);
However, Yue does not explicitly teach:
setting first input data and the first target condition specifying a target value of an output of an objective function;
Held teaches:
setting first input data and the first target condition specifying a target value of an output of an objective function (see Held, Paragraph [0094], “ applying a measurement-based Bayesian optimization, BO. The proposed objective function contains all the target specifications that the user would like to optimize—together and in combination—thus, the optimization will be a trade-off between all the defined specifications.” [The user may set the target specifications (i.e., setting … the first target condition specifying a target value of an output of an objective function) that that user would like to optimize.]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Yue (teaching user interfaces for high-dimensional design problems) in view of Held (teaching calculating process parameters), and arrived at a method that incorporates target specifications. One of ordinary skill in the art would have been motivated to make such a combination for the purposes of optimizing variables (see Held, Paragraph [0070]). In addition, both the references (Yue and Held) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as Bayesian optimization. The close relation between both the references highly suggests an expectation of success.
The combination of Yue, and Held further teaches:
generating, using a first function that models the objective function, a predicted first output data corresponding to the first input data and relative to the target value (see Yue, Pages 5, 9-13, “we explain how to derive the predictive distribution called Gaussian process regression (GPR), which is often used as the basis of BO. … GPR enables us to calculate the predictive distribution of the goodness value at any unseen data point 𝒛∗ as follows. 𝑔(𝒛∗) ∼ N (𝜇𝑛 (𝒛∗), 𝜎2 𝑛 (𝒛∗)), (21) where 𝜇𝑛 and 𝜎 2 𝑛 are the predicted mean and variance functions given 𝑛 observed data, respectively. The advantage of GPR is to easily calculate 𝜇𝑛 and 𝜎 2 𝑛 in a closed form. In addition to calculating the expected predictive value of 𝑔(𝒛∗) with 𝜇𝑛 (𝒛∗), we can also calculate the uncertainty of the prediction with 𝜎 2 𝑛 (𝒛∗) for any input 𝒛∗.” [The gaussian process regression method (i.e., a first function) is used to predict the first output data corresponding to the data points (i.e., first input data and relative to the target value).]);
and generating, using a second function that provides a result of comparison between the predicted first output data and the first target condition, second input data that derives, using the first function, an output data closer to the target value compared to the predicted first output data (see Yue, Pages 10-12, Figure 2, “BO uses GPR to determine the next sampling point, 𝒛 (𝑡+1). That is, BO performs GPR using the data D𝑡 to estimate the objective function shape as reasonably as possible. Based on the estimated function, BO determines the most “effective” data point as the next sampling point … For this purpose, BO performs GPR to estimate the objective function (top) and the calculates the acquisition function (bottom) … Using one of such acquisition functions, the next sampling point is determined … The basic concept of the acquisition function is to balance exploration (i.e., prioritize as unvisited regions as possible) and exploitation (i.e., prioritize as high-value regions as possible). Using one of such acquisition functions, the next sampling point is determined as ... ut(z)-g(zt+).” [The next sampling point is determined (i.e., second input data) using the acquisition functions (i.e., second function). In equation (37), “ut(z)-g(zt+)” is the difference (i.e., comparison) between the first output data and the first target condition.]),
wherein the second function comprises a first component and a second component, where the first component corresponds to a difference between a mean value of the predicted first output data and the target value and the second component corresponds to a standard deviation value of the predicted first output data (see Yue, Page 12, equation 37, “ut(z)-g(zt+) … 𝜎𝑡 (z).” [ut(z)-g(zt+) (i.e., first component corresponding to a difference between a mean value of the first output data and the first target value), and 𝜎𝑡 (z) (i.e., second component corresponding to a standard deviation value of the first output data).]).
Regarding claim 2, Yue in view of Held teaches all the limitations of claim 1. Held further teaches:
wherein the first target condition comprises a first target value (see Held, Paragraph [0094], “ applying a measurement-based Bayesian optimization, BO. The proposed objective function contains all the target specifications that the user would like to optimize—together and in combination—thus, the optimization will be a trade-off between all the defined specifications.” [The user may set the target specifications (i.e., setting … the first target condition specifying a target value of an output of an objective function) that that user would like to optimize.]).
Regarding claim 3, Yue in view of Held teaches all the limitations of claim 1. Yue further teaches:
wherein the first target condition comprises a first target range, and the generating of the second input data comprises generating, to be the second input data, input data that derives, using the first function, output data within the first target range in response to the predicted first output data not being within the first target range (see Yue, Pages 5, 10, “One way to quantify uncertainty is to estimate the spread of prediction, i.e., to estimate not only the predicted value but also the possible range of the predicted value for an unknown input. In statistical modeling, we typically model this possible range as a variance. Thus, let us consider the variance function 𝜎 2 (𝑥) which outputs the variance of the prediction for any input 𝑥 and the mean function 𝜇(𝑥) which outputs the expected predictive value for any input 𝑥. … , the acquisition function maximization problem is formulated by restricting the possible range of 𝒛 to the hypercube [0, 1] 𝑑 ⊂ R” [A first target range may be predicted.]).
Regarding claim 5, Yue in view of Held teaches all the limitations of claim 1. Yue further teaches:
wherein the generating of the second input data comprises generating the second input data by applying different weights to the first component and the second component (see Yue, Page 12, “where PDF(·) and CDF(·) denote the probabilistic density function and the cumulative distribution function of the standard norm” [PDF(·) and CDF(·) are the different weights applied to first component and second component).]).
Regarding claim 6, Yue in view of Held teaches all the limitations of claim 1. Yue further teaches:
wherein input data is repetitively determined through gradual target conditions comprising the first target condition (see Yue, Page 10, Figure 2, “BO iteratively evaluates function values to seek better solutions. … Illustration of a BO process. Suppose that we have so far observed three data points, 𝒛 (1), 𝒛 (2), and 𝒛 (3), and we want to determine the location of the next sampling point 𝒛 (4). For this purpose, BO performs GPR to estimate the objective function (top) and then calculates the acquisition function (bottom). The next sampling point is defined as the maximizer of the acquisition function.” [The data points are iteratively determined through gradual target conditions.]).
Regarding claim 7, Yue in view of Held teaches all the limitations of claim 1. Yue further teaches:
setting a second target condition; generating predicted second output data corresponding to the second input data using the first function; and generating third input data using the second function (see Yue, Page 10, Figure 2, “BO iteratively evaluates function values to seek better solutions. … Illustration of a BO process. Suppose that we have so far observed three data points, 𝒛 (1), 𝒛 (2), and 𝒛 (3), and we want to determine the location of the next sampling point 𝒛 (4). For this purpose, BO performs GPR to estimate the objective function (top) and then calculates the acquisition function (bottom). The next sampling point is defined as the maximizer of the acquisition function.” [The data points are iteratively determined through gradual target conditions.]).
Regarding claim 8, Yue in view of Held teaches all the limitations of claim 1. Yue further teaches:
providing a user interface, wherein the user interface comprises: a first section configured to display a plurality of points of reference (PORs) corresponding to different input data and to receive a first user input of selecting a first POR corresponding to the first input data among the plurality of PORs; a second section configured to display the first input data corresponding to the first user input and to receive a second user input of modifying the first input data; a third section configured to display the predicted first output data based on the first function; the fourth section configured to display a settable condition and to receive a third user input of setting the first target condition; and a fifth section configured to display recommended input data comprising the second input data based on the second function (see Yue, Pages 3, 16, , 19, 21, 24, 28, Figures 3-4, 6, 8, 10, 13, “We focus on the development of effective user interfaces for such high-dimensional design problems, where, mathematically, the user has control of a set of parameters 𝒛 = (𝑧1, . . . , 𝑧𝑑 )⊤ over a design space Z ⊂ R 𝑑 to generate the output 𝒙 = 𝒇 (𝒛) of the design, with the quality of each design judged by the user’s own goodness function 𝑔(𝒙) = 𝑔(𝒇 (𝒛)), or 𝑔(𝒛) for short.” [The figures show different user interfaces that allows the user to input and display data.]).
Regarding claim 9, Yue in view of Held teaches all the limitations of claim 8. Yue further teaches:
wherein the third section comprises a first graph representing the predicted first output data according to the first input data, and in response to the first input data being modified according to the second user input, the first output data is changed based on the first function, and the first graph is updated based on the modified first input data and the changed first output data. (see Yue, Pages 3, 16, , 19, 21, 24, 28, Figures 3-4, 6, 8, 10, 13, “We focus on the development of effective user interfaces for such high-dimensional design problems, where, mathematically, the user has control of a set of parameters 𝒛 = (𝑧1, . . . , 𝑧𝑑 )⊤ over a design space Z ⊂ R 𝑑 to generate the output 𝒙 = 𝒇 (𝒛) of the design, with the quality of each design judged by the user’s own goodness function 𝑔(𝒙) = 𝑔(𝒇 (𝒛)), or 𝑔(𝒛) for short.” [The figures show different user interfaces that allows the user to input and display data.]).
Regarding claim 10, Yue in view of Held teaches all the limitations of claim 8. Yue further teaches:
wherein the fifth section comprises a second graph representing a degree of target achievement and uncertainty of each recommended input data, and in response to recommended input data corresponding to the second input data being selected from the second graph, the second section and the third section are updated based on the second input data (see Yue, Pages 3, 16, , 19, 21, 24, 28, Figures 3-4, 6, 8, 10, 13 “We focus on the development of effective user interfaces for such high-dimensional design problems, where, mathematically, the user has control of a set of parameters 𝒛 = (𝑧1, . . . , 𝑧𝑑 )⊤ over a design space Z ⊂ R 𝑑 to generate the output 𝒙 = 𝒇 (𝒛) of the design, with the quality of each design judged by the user’s own goodness function 𝑔(𝒙) = 𝑔(𝒇 (𝒛)), or 𝑔(𝒛) for short.” [The figures show different user interfaces that allows the user to input and display data.]).
Regarding claims 11-20, Yue in view of Held teaches all of the limitations of claims 1-3, and 5-10 in method form rather than in non-transitory computer readable medium and apparatus form. Yue also discloses a non-transitory computer readable medium [page 3] and an apparatus [page 3]. Therefore, the supporting rationale of the rejection to claim 1-3, and 5-10 applies equally as well to those limitations of claims 11-20.
Response to Arguments
Applicant’s Arguments, filed September 25th, 2025, have been fully considered, but are not persuasive.
Applicant argues on pages 9-12 of Applicant's Remarks that the amended claims are integrated into a practical application. The Examiner respectfully disagrees.
The abstract idea is not integrated into a practical application because the invention merely uses the computer such as a “processor,” and “user interface” to facilitate performance of the abstract idea, and the specification (paragraphs [0003], [0047], [0048], [0049] and [0050] as cited by the Applicant) does not provide sufficient details such that one of ordinary skill in the art would recognize that the invention as claimed is an improvement, thus, it does not improve the functioning of the computer in any way (see MPEP 2106.04(d)(1)). Accordingly, the claims do not apply the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment (see MPEP 2106.04(e)).
Applicant argues on pages 17-22 of Applicant's Remarks that the cited references do not teach or suggest “a user interface comprising a fourth section configured to receive a third user input of setting a first target condition,” “setting first input data,” and “a target value being set, a comparison between the predicted first output data and the first target condition, a difference between a mean value of the predicted first output data and the target value, and a standard deviation value of the predicted first output data” (emphasis added).
Applicant’s Arguments, filed September 25th, 2025, have been fully considered, but are moot in light of the new grounds of rejection.
For the above reasons, it is believed that the rejections should be sustained.
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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/HUSAM TURKI SAMARA/
Examiner, Art Unit 2161
/APU M MOFIZ/Supervisory Patent Examiner, Art Unit 2161