DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Action is final and is in response to the claims filed 11/07/2025. Claims 1-20 are currently pending, of which claims 1-20 are currently rejected.
Response to Arguments
Specification Objections: Objection to the specification is withdrawn necessitated by amendment to the specification.
Drawings Objections: Objection to the drawings has been withdrawn. However, see new drawing objection necessitated by amendments.
35 U.S.C. 112(b): Claim rejections under 35 U.S.C. 112(b) have been withdrawn necessitated by amendments.
35 U.S.C. 103: Applicant’s arguments regarding the 35 U.S.C. 103 rejections have been fully considered.
Applicant argues in pages 8 and 9 that Najafi does not teach the mixing of two different types of numbers. Specifically, Applicant argues “For the claim element as previously presented, the Office Action first cites Najafi paragraphs [0111], the accumulator concatenating two random numbers to produce one final random number. Yet that is unrelated to the recited claim of mixing bits from two different types of numbers, one is from a sequence of white noise random numbers and the other from a sequence of quasi-random numbers: "mixing bits from the sequence of white noise random numbers with bits from the intermediate sequence of quasi-random numbers to produce a final sequence of quasi-random numbers."”
Examiner respectfully disagrees. Examiner did not state that Najafi alone teaches mixing white noise numbers with quasi-random numbers. Instead, NPLs Goda and Wolfe are relied on to cover these limitations. Goda teaches how it is less computationally expensive to perform concatenate quasi monte carlo rules with plain monte carlo rules, and Wolfe teaches plain monte carlo being considered white noise. See 35 U.S.C. 103 rejection below.
Applicant further argues at the bottom of page 9 and in page 10 that Goda does not teach mixing bits from quasi monte carlo and plain monte carlo. Applicant specifically argues “In other words, each sample point in the final concatenated point set is either a sample point from QMC (the first d dimensions or from the plain Monte Carlo (the remaining dimensions). There is no sample point in the final concatenated point set of Goda where bits of a sample point produced by mixing a first number of a PMC sequence and a second number from a plain Monte Carlo sequence.”
Examiner respectfully disagrees. Goda teaches concatenating (mixing) quasi monte carlo point sets and plain monte carlo point sets. Goda discloses using CBC algorithm for generating QMC point sets for the first d coordinates, and remaining s-d coordinates with less relative importance are filled by random points. See: Goda Page 2, third paragraph. Combination of Goda and Nafaji would cause for the generators disclosed by Nafaji to use plain monte carlo rules to generate random numbers for the lower bits (remaining of the point sets as disclosed by Goda). Additionally, Wolfe states plain monte carlo can be considered white noise. See 35 U.S.C. 103 rejection below.
Additionally, Applicant argues at the bottom of page 10 and in page 11 that Wolfe does not teach quasi-random sequence generation for rendering operations in graphic processors. Specifically, Applicant argues “Furthermore, Wolfe does not cure the deficiency of Najafi and fails to teach or suggest "quasi-random sequence generation logic to generate a quasi-random number sequence to be used by the graphics processor for rendering operations" as recited in amended claim 1. The Office Action cites Wolfe page 2 last paragraph about the GPU performing rending operation, yet that paragraph does not mention any quasi-random numbers; similarly, the cited Wolf first paragraph does not mention what the random candidate points are. However, the conclusion in Wolfe does specify that the random numbers produced in the GPU are pseudo-random numbers by pseudo-random number generators (PRNGs)”
Examiner will interpret arguments directed to Wolfe to be directed to Askar since applicant quotes the title and the conclusion of Askar. Arguments directed to Askar are determined to be persuasive. However, see new grounds of rejections necessitated by amendments causing the execution resources and quasi-random sequence generation logic to be inside the graphics processor.
Claim Objections
Claims 3, 8, 12 and 18 objected to because of the following informalities:
Claim 3: wherein mixing the bits further comprises
Claim 8: wherein the intermediate sequence of quasi-random numbers is based on the sequence of white noise random numbers.
Claim 12: wherein mixing the bits further comprises
Claim 18: wherein the intermediate sequence of quasi-random numbers is based on the sequence of white noise random numbers.
Appropriate correction is required.
Drawings
The drawings are objected to under 37 CFR 1.83(a). The drawings must show every feature of the invention specified in the claims. Therefore, the “wherein each quasi-random number in the final sequence of quasi-random numbers includes bits mixed from a white noise random number of the sequence of white noise random numbers and a quasi-random number from the intermediate sequence of quasi-random numbers” limitation first disclosed in claim 1 must be shown or the feature(s) canceled from the claim(s). No new matter should be entered.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 9 and 15 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 9 describes “wherein bits from a second white noise random number is scrambled to generate the quasi-random number.” However, there is no indication in the original disclosure that there is a “second white noise random number” scrambled to generate the quasi-random number. Paragraph 00924 - 00926 in the specification discloses a 32-bit Sobol number and a 32-bit white noise number concatenated to form a 64-bit random number, however it does not disclose a second white noise number scrambled to generate a quasi-random number.
Claim 15 discloses similar language as claim 9, and is rejected for the same reason therein.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2-9 and 11-18 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 2 recites the limitation "a quasi-random number" in the second line of claim 2. It is unclear if applicant means for this quasi-random number to be the “a quasi-random number” disclosed in claim 1, which claim 2 depends on. There is insufficient antecedent basis for this limitation in the claim.
Claims 3-9 inherit the same deficiency by reason of dependence. They are rejected for the same reason as claim 2.
Claim 11 recites the limitation "a quasi-random number" in the second line of claim 11. It is unclear if applicant means for this quasi-random number to be the “a quasi-random number” disclosed in claim 10, which claim 11 depends on. There is insufficient antecedent basis for this limitation in the claim.
Claims 12-18 inherit the same deficiency by reason of dependence. They are rejected for the same reason as claim 11.
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.
Claims 1-6 are rejected under 35 U.S.C. 103 as being unpatentable over Najafi et al. (U.S. Patent Application Publication 20200401376 A1), hereinafter “Najafi”, in view of Samuli Laine in NPL: “Megakernels Considered Harmful: Wavefront Path Tracing on GPUs” (https://dl.acm.org/doi/pdf/10.1145/2492045.2492060), hereinafter “Laine”, in view of Takashi Goda in NPL: A note on concatenation of quasi-Monte Carlo and plain Monte Carlo rules in high dimensions (https://arxiv.org/pdf/2106.12184), hereinafter “Goda”, further in view of Alan Wolfe in NPL: Generating Random Numbers From a Specific Distribution With The Metropolis Algorithm (MCMC) (https://blog.demofox.org/2019/05/25/generating-random-numbers-from-a-specific-distribution-with-the-metropolis-algorithm-mcmc/), hereinafter “Wolfe”.
Regarding Claim 1, Najafi teaches:
An apparatus comprising:
…
quasi-random sequence generation logic to generate a quasi-random number sequence (Fig. 11; ¶0103, e.g., Generators 1130A-1130C (quasi-random sequence logic) convert respective portion of a binary number into a quasi-random bit-stream)) … the quasi-random sequence generation logic to perform:
…
generating an intermediate sequence of quasi-random numbers (Fig. 11; ¶0103, e.g., Generators 1130A - 1130N output bit streams (intermediate sequence of quasi-random numbers) corresponding to each portion of the split binary numbers; ¶0101, e.g., Binary numbers are split into two portions, which are the 4 MSBs (intermediate portion) and 4 LBSs); and
mixing bits from the sequence of [quasi] random numbers with bits from the intermediate sequence of quasi-random numbers (¶0111, e.g., Accumulator 1170 can concatenate (mixing bits) of sub-results (quasi random numbers) to produce result 1180 (final quasi random number)) to produce a final sequence of quasi-random numbers (¶0031, e.g., New iteration of input data is inputted, hence new values are generated (final sequence of quasi-random numbers)), wherein each quasi-random number in the final sequence of quasi-random numbers includes bits mixed from a [quasi] random number of the sequence of [quasi] random numbers and a quasi-random number from the intermediate sequence of quasi-random numbers ((¶0101, e.g., Binary numbers are split into two portions, which are the 4 MSBs (intermediate portion) and 4 LBSs; ¶0111, e.g., Accumulator 1170 can concatenate (mixing bits) of sub-results (quasi random numbers) to produce result 1180 (final quasi random number)).
Najafi does not teach:
a graphics processor comprising:
execution resources to execute graphics instructions;
quasi-random sequence generation logic to generate a quasi-random number sequence to be used by the graphics processor for rendering operations, …
the quasi-random sequence generation logic to perform:
generating a sequence of white noise random numbers;
generating an intermediate sequence of quasi-random numbers; and mixing bits from the sequence of white noise random numbers with bits from the intermediate sequence of quasi-random numbers to produce a final sequence of quasi-random numbers, wherein each quasi-random number in the final sequence quasi-random numbers includes bits mixed from a white noise random number of the sequence of white noise random numbers and a quasi-random number from the intermediate sequence quasi-random numbers.
However, Laine teaches:
a graphics processor comprising:
execution resources to execute graphics instructions (Page 137, First Column, Section 1 Introduction, e.g., GPU (graphics processor) uses threads (execution resources) to execute instructions);
quasi-random sequence generation logic to generate a quasi-random number sequence (Page 139, Second column, Paragraph above section 4.1, e.g., quasirandom number generation is implemented on the GPU) to be used by the graphics processor for rendering operations (Page 137, Second column, fourth paragraph, e.g., GPU performs rendering operations)
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to which said subject matter pertains to combine GPU using threads to perform quais random number generation as taught by Laine with the computational unit 100 as taught by Najafi. One would have been motivated to combine these references because both references disclose quasi random number generation in processors, and Laine enhances the model of Najafi by making quasi random number generation on GPUs cost negligible (See Laine: Page 139, Second column, Paragraph above section 4.1)
Najafi in view of Laine do not teach:
the quasi-random sequence generation logic to perform:
generating a sequence of white noise random numbers;
generating an intermediate sequence of quasi-random numbers; and mixing bits from the sequence of white noise random numbers with bits from the intermediate sequence of quasi-random numbers to produce a final sequence of quasi-random numbers, wherein each quasi-random number in the final sequence quasi-random numbers includes bits mixed from a white noise random number of the sequence of white noise random numbers and a quasi-random number from the intermediate sequence quasi-random numbers.
However, in the same field of endeavor, Goda teaches how it is less computationally expensive to use concatenated quasi-Monte Carlo and plain Monte Carlo rules instead of component-by-component (CBC) construction of Quasi-Monte Carlo point sets (See Goda: abstract and introduction). Specifically, Goda explains “using a rank-1 lattice point set for the first d coordinates with some 0 < d < s and random points for the remaining s − d coordinates" (Goda: Page 4, Top paragraph). Additionally, Najafi teaches that Generators 1130A-1130C (quasi-random sequence generation logic) may generate pseudo-random numbers, which can be considered a plain Monte Carlo method since it generates random numbers that are not quasi random number, or quasi-Monte Carlo. See Najafi: ¶0104. Therefore, combining the method of using plain monte carlo random points with the generators 1130 as taught by Najafi would cause for the generators to perform the operation of generating a sequence of [plain Monte Carlo] random numbers;
It would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to which said subject matter pertains to combine method of generating random numbers for the remaining coordinates as taught by Goda with the Generators 1130 for the lowest bits of each binary number as taught by Najafi in view of Laine. One would have been motivated to combine these references because both references disclose quasi random number generation, and Goda enhances the model of Najafi in view of Laine by "exploiting a decay of the weights of function spaces, almost the optimal order of the mean squared worst-case error is achieved by such a concatenated quadrature rule as long as d scales at most linearly with the number of points", and because "This result might be useful for numerical integration in extremely high dimensions, such as partial differential equations with random coefficients for which even the standard fast component-by-component algorithm is considered computationally expensive." (Goda: Abstract)
Najafi in view of Laine in view of Goda do not teach:
the quasi-random sequence generation logic to perform:
generating a sequence of white noise random numbers;
generating an intermediate sequence of quasi-random numbers; and mixing bits from the sequence of white noise random numbers with bits from the intermediate sequence of quasi-random numbers to produce a final sequence of quasi-random numbers, wherein each quasi-random number in the final sequence quasi-random numbers includes bits mixed from a white noise random number of the sequence of white noise random numbers and a quasi-random number from the intermediate sequence quasi-random numbers.
However, in the same field of endeavor, Wolfe teaches how plain Monte Carlo numbers can be represented by white noise. Wolfe explains “Something I find interesting is that plain Monte Carlo uses white noise, quasi Monte Carlo uses low discrepancy sequences (and i think blue noise would fit in here), while Metropolis MCMC uses a random walk, which is red noise” (Links and Closing, Second Paragraph)
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to which said subject matter pertains to combine the method of using white noise for plain Monte Carlo as taught by Wolfe with the plain Monte Carlo generators as taught by Najafi in view of Laine in view of Goda. One would have been motivated to combine these references because both references disclose Quasi and plain Monte Carlo random numbers, and Wolfe enhances the model of Najafi in view of Askar in view of Goda because “white noise generalizes to any dimension” (Wolfe: Integration, Last Paragraph). Hence, Najafi in view of Laine in view of Goda in view of Wolfe teach Claim 1 in its entirety.
With regards to Claim 2, Najafi in view of Laine in view of Goda in view of Wolfe teach:
The apparatus of claim 1 wherein bits from a first white noise random number are mixed with bits from a quasi-random number (Goda: Page 4, Top paragraph, e.g., Rank-1 lattice point set (quasi-random number sequence) is used for the first d coordinates, then Random points (plain monte carlo) are used for the remaining s - d coordinates; Wolfe: Links and Closing, Second Paragraph, e.g., plain monte carlo uses white noise; Najafi: ¶0110, e.g., MSB portion of a binary number (quasi-random number) and LSB portion of a binary number [White noise random number as taught by Goda in view of Wolfe] are operated on (mixed) to produce a sub-result (first portion)) to generate a first portion of a final quasi-random number in the final sequence of quasi-random numbers (Najafi: ¶0111, e.g., Accumulator 1170 concatenates sub-results (including first and second portions) and outputs result 1180 (final quasi-random numbers); ¶0031, e.g., New iteration of input data is inputted, hence new values are generated (final sequence of quasi-random numbers)).
The motivation to combine provided with respect to claim 1 applies equally to claim 2.
With regards to Claim 3, Najafi in view of Laine in view of Goda in view of Wolfe teach:
The apparatus of claim 2, wherein the mixing of the bits further comprising:
shifting bits of the quasi-random number to generate a second portion of the final quasi-random number in the final sequence of quasi-random numbers (Najafi: ¶0111, e.g., Sub-result (second portion) is determined using two MSB portions (quasi-random number) and has an offset value of eight bits. Bit streams are converted to binary numbers and shifted by offset values (second portion) by Accumulator 1170; ¶0031, e.g., New iteration of input data is inputted, hence new values are generated (final sequence of quasi-random numbers)).
With regards to Claim 4, Najafi in view of Laine in view of Goda in view of Wolfe teach:
The apparatus of claim 3 wherein the first portion and the second portion are concatenated to form the final quasi-random number in the final sequence of quasi-random numbers (Najafi: ¶0111, e.g., Accumulator 1170 concatenates sub-results (including first and second portions) to output result 1180 (final quasi-random number); ¶0031, e.g., New iteration of input data is inputted, hence new values are generated (final sequence of quasi-random numbers)).
With regards to Claim 5, Najafi in view of Laine in view of Goda in view of Wolfe teach:
The apparatus of claim 4, wherein the first portion and the second portion have an equal number of bits (Najafi: ¶0101, e.g., Binary numbers are split into two portions, which are the 4 MSBs and 4 LBSs).
With regards to Claim 6, Najafi in view of Laine in view of Goda in view of Wolfe teach:
The apparatus of claim 1, wherein the intermediate sequence of quasi- random numbers comprises a Sobol sequence (Najafi: ¶0025).
With regards to Claim 8, Najafi in view of Laine in view of Goda in view of Wolfe teach:
The apparatus of claim 1, wherein the intermediate sequence of quasi-random numbers based on the sequence of white noise random numbers (Goda: Page 4, Top paragraph, e.g., Rank-1 lattice point set (quasi-random number sequence) is used for the first d coordinates, then Random points (plain monte carlo) are used for the remaining s - d coordinates; Wolfe: Links and Closing, Second Paragraph, e.g., plain monte carlo uses white noise; Najafi: ¶0110, e.g., MSB portion of binary numbers (intermediate sequence of quasi-random numbers) and LSB portion of binary numbers [White noise random numbers as taught by Goda in view of Wolfe] form one operand (based on sequence of white noise random numbers)).
Regarding claims 11-14, 16 and 18, they are method claims practiced by the apparatus of claims 2-4, 6 and 8, respectively. They are rejected for the same reasons as claims 2-4, 6 and 8.
Regarding claims 19 and 20, they are media claims practiced by the apparatus of claims 1 and 2. They are rejected for the same reasons as claims 1 and 2.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Najafi in view of Laine in view of Goda in view of Wolfe, further in view of Steve Rotenberg in NPL: “Random Numbers and Mappings” (https://cseweb.ucsd.edu/classes/sp17/cse168-a/CSE168_07_Random.pdf), hereinafter “Rotenberg”.
With regards to Claim 7, Najafi in view of Laine in view of Goda in view of Wolfe teach:
The apparatus of claim 1, wherein the quasi-random number sequence is used by the graphics processor for the rendering operations (Laine: Page 139, Second column, Paragraph above section 4.1, e.g., quasirandom number generation is implemented on the GPU; Page 137, Second column, fourth paragraph, e.g., GPU performs rendering operations)
Najafi in view of Laine in view of Goda in view of Wolfe do not teach:
The apparatus of claim 1, wherein the quasi-random number sequence is used by the graphics processor for the rendering operations that comprise adaptive sampling for ray tracing operations.
However, Rotenberg teaches:
wherein the quasi-random number sequence is used … for the rendering operations that comprise adaptive sampling for ray tracing operations (Page 20, e.g., Quasi-random numbers are useful for ray tracing, and have good potential for use in adaptive sampling schemes).
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to which said subject matter pertains to combine the method of using quasi-random numbers for ray tracing and adaptive sampling schemes as taught by Rotenberg with the quasi-random number generators in a GPU as taught by Najafi in view of Laine in view of Goda in view of Wolfe. One would have been motivated to combine these references because both references disclose quasi-random number generation for rendering operations, and Rotenberg enhances the model of Najafi in view of Laine in view of Goda in view of Wolfe because “They offer the ability to generate well spaced sequences similar to jitter patterns but without requiring knowledge ahead of time of how many samples are needed” (Rotenberg: Page 20)
Regarding claim 17, it is a method claim practiced by the apparatus of claim 7. It is rejected for the same reasons as claim 7.
Claims 9 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Najafi in view of Laine in view of Goda in view of Wolfe, further in view of Macro Rao in NPL: “EXPERIMENT FINDINGS WITH A LINEAR CONGRUENTIAL GENERATOR VARIANT” (https://www.researchgate.net/publication/331546037_Experimental_findings_with_a_linear_congruential_generator_variant), hereinafter “Rao”
With regards to Claim 9, Najafi in view of Laine in view of Goda in view of Wolfe teach the apparatus of claim 2. They do not teach:
wherein bits from a second white noise random number is scrambled to generate the quasi-random number.
However, in the same field of endeavor, Rao teaches how scrambling quasi-random point sets is useful for improving uniformity. Rao explains “Because of the way in which quasi-random sequences are generated, they may contain undesirable correlations, especially in their initial segments, and especially in higher dimensions. To address this issue, quasi-random point sets often skip, leap over, or scramble values in a sequence”. (Rao: Page 10, Last paragraph)
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to which said subject matter pertains to combine the method for scrambling quasi random point sets as taught by Rao with the generators 1130 using quasi random number generation as taught by Najafi in view of Laine in view of Goda in view of Wolfe. One would have been motivated to combine these references because both references disclose Quasi random number generation, and Rao enhances the model of Najafi in view of Laine in view of Goda in view of Wolfe because “scrambling reduces correlations while also improving uniformity.” (Rao: Page 10, Last paragraph)
Regarding claim 15, it is a method claim practiced by the apparatus of claim 9. It is rejected for the same reasons as claim 9.
Prior Art Made of Record
US 12400118 B2 (Keller et al.) – teaches Sobol low discrepancy sequence used for neural network layer linking by set of permutations based on the Sobol sequence. This method may be executed by a GPU. See Column 33 Line 12 - Column 35 Line 51.
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 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CARLOS H DE LA GARZA whose telephone number is (571)272-0474. The examiner can normally be reached Monday-Friday 9:30AM-6PM.
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/C.H.D./
Carlos H. De La GarzaExaminer, Art Unit 2182 (571)272-0474
/EMILY E LAROCQUE/Primary Examiner, Art Unit 2182