DETAILED ACTION
Claims 1-20 are presented for examination
This office action is in response to submission of application on 28-FEBURARY-2023.
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 .
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to
an abstract idea (Abstract Idea) without significantly more.
Regarding claim 1, in Step 1 of the 101 analysis set forth in MPEP 2106, the claim recites a method for tuning a language model. A method is one of the four statutory categories of invention.
In Step 2a Prong 1 of the 101 analysis set forth in the MPEP 2106, the examiner has determined
that the following limitations recite a process that, under the broadest reasonable interpretation, covers
a mental process but for recitation of generic computer components:
identifying one or more proxy elements of a plurality of elements in the tuning data that correlate with one or more identity elements associated with training bias, the identifying comprising: (one can mentally identify a set of elements within a group of elements as a process of simply evaluating the elements and making a determination based of the elements)
computing respective correlation scores for at least a portion of elements of the plurality of elements; (the method will calculate a score by using the elements in a formula for calculation and is therefore a mathematical concept (MPEP 2106.04(a)(2)))
and selecting particular elements of the at least a portion of elements with respective correlation scores that exceed a correlation threshold as proxy elements; (one can mentally select a set of elements within a group of elements based on a score as a process of simply evaluating the score and making a determination based of the score)
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. According, the claim “recites” an abstract idea.
In Step 2a Prong 2 of the 101 analysis set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
A method, comprising: (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))
receiving tuning data to tune a pre-trained language model; (Adding insignificant extra-solution activity (mere data gathering) to the judicial exception (MPEP 2106.05(g))
replacing the identified one or more proxy elements and one or more identity elements in the tuning data with masking elements to generate masked tuning data; (Adding insignificant extra-solution activity (mere data gathering) to the judicial exception (MPEP 2106.05(g))
and tuning the pre-trained language model with the masked tuning data to generate a tuned language model with reduced bias. (In step 2A prong 2 tuning a model is a mere application of a computer tool (M.L. Model), which is not indicative of integration into a practical application. In step 2B, merely applying a computer tool is not indicative of significantly more.)
Since the claim does not contain any other additional elements that are indicative of integration into a practical application, the claim is “directed” to an abstract idea.
In Step 2b of the 101 analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above, the additional element (iv) recites generally linking the use of the judicial exception to a particular technological environment or field of use, (v) and (vi) recites mere data gathering, (vii) recites a mere application of a computer tool which is not indicative of significantly more. Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Regarding claim 2, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 2 recites The method of claim 1, wherein the correlation threshold is determined according to a probability proportional to a bias score. (In step 2A, prong 1, this recites an abstract idea but for recitation of generic computer components which is not indicative of integration into a practical application.) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 3, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 3 recites The method of claim 1, wherein the correlation threshold of a particular element of the plurality of elements is determined according to a probability proportional to a correlation score of the particular element. (In step 2A, prong 1, this recites an abstract idea but for recitation of generic computer components which is not indicative of integration into a practical application.) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 4, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 4 recites The method of claim 1, wherein the at least a portion of elements of the plurality of elements for which respective correlation scores are computed comprises individual tokens of the tuning data. (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 5, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 5 recites The method of claim 1, wherein the at least a portion of elements of the plurality of elements for which respective correlation scores are computed comprises individual tokens of the tuning data within sentences also including identity elements. (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 6, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 6 recites The method of claim 1, wherein computing a correlation score for particular element of the individual elements comprises: (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)) computing, for individual elements of the one or more identity elements associated with training bias, respective ratios of respective probabilities of joint distribution with respect to respective probabilities of individual distribution; (In step 2A, prong 1, this recites an mathematical concept but for recitation of generic computer components which is not indicative of integration into a practical application.) and assigning a highest ratio of the respective ratios as the correlation score. (In step 2A, prong 1, this recites an abstract idea but for recitation of generic computer components which is not indicative of integration into a practical application.) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 7, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 7 recites The method of claim 1, further comprising: (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)) receiving a dictionary defining the one or more identity elements associated with training bias prior to identifying one or more proxy elements of a plurality of elements in the tuning data that correlate with one or more identity elements associated with training bias; receiving, subsequent to generating the tuned language model, a updated dictionary with a one or more different identity elements associated with training bias, and responsive to receiving the updated dictionary: generating updated masked tuning data; (Adding insignificant extra-solution activity (mere data gathering) to the judicial exception (MPEP 2106.05(g)) and generating an updated tuned language model with reduced bias using the updated masked tuning data. (In step 2A prong 2 generating an updated model is a mere application of a computer tool (M.L. Model), which is not indicative of integration into a practical application. In step 2B, merely applying a computer tool is not indicative of significantly more.) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
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 1-2, 8-9, and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over GARIMELLA (U.S. Pub. No. US 20220147713 A1) in view of TAN (U.S. Pub. No. US 20180367423 A1)
Regarding claim 1, GARIMELLA teaches the invention substantially as claimed, including:
A method, comprising: receiving tuning data to tune a pre-trained language model; identifying one or more proxy elements of a plurality of elements in the tuning data that correlate with one or more identity elements associated with training bias, the identifying comprising: ([0027] In one implementation pretrained language model 100 undergoes equalization training 104 and/or de-clustering training 106. Equalization training 104 involves incorporating an equalization loss 110 into pretrained language model 100 and retraining using a small training corpus 126, thus resulting in an equalized language model. Equalization training 104 uses an equalization loss function that attempts to equalize the associations of words that are nominally neutral (for example, “doctor”) with words that define a group (for example, “she” or “he”).) computing respective correlation scores for at least a portion of elements of the plurality of elements; ([0063] In some cases it may be desired to evaluate the extent to which bias has been mitigated using the techniques disclosed herein. For example, a bias evaluation module 667 can be configured to evaluate bias in debiased text 142 and/or in debiased language model 108. See reference numeral 560 in FIG. 5. A wide range of bias evaluation metrics 170 can be used in this regard. One example bias evaluation metric 170 that can be used to quantify bias in generated text is the constrained co-occurrence score CCO, which can be expressed as:
[00006]CCO(text)=1N.Math.w∈N.Math.log(.Math.a∈Ac(w,a).Math.b∈Bc(w,b)).Math..(6)
Here N is the set of adjectives and adverbs in text, A is the set of dimension definitional word pairs that define a first group (for example, the set {she, woman, herself, sister, girl}), B is the set of dimension definitional word pairs that define a second group (for example, the set {he, man, himself, brother, boy}), c(w, d) gives the number of cooccurrences of word w with words of dimension d in its context. As used herein, two words are understood to “cooccur” when they are within a n words of each other in generated text, where n is referred to as a context window. In one implementation, context window n=10 words, although other context windows can be used in other implementations, such as n=2, 5, 8, 9, 11, 12, 15, 18, or 20 words. Other values of n can be used in other implementations. According to this metric, CCO(text)∈{0, ∞}, with higher values indicating more bias present in text. Additional details regarding other bias evaluation metrics will be disclosed in conjunction with the experimental results described in turn.)
While GARIMELLA does teach receiving element data and finding a correlation score, it does not explicitly teach:
and selecting particular elements of the at least a portion of elements with respective correlation scores that exceed a correlation threshold as proxy elements; replacing the identified one or more proxy elements and one or more identity elements in the tuning data with masking elements to generate masked tuning data;
However, in analogous art that similarly handles correlated data, TAN teaches:
and selecting particular elements of the at least a portion of elements with respective correlation scores that exceed a correlation threshold as proxy elements; ([0119] (37) The client calculates similarity between all target data and the new data to obtain maximum similarity and target data corresponding to the maximum similarity.
[0120] (38) The client determines whether the maximum similarity is greater than a preset threshold.
[0121] (39) When the maximum similarity is greater than the preset threshold, the client replaces the target data corresponding to the maximum similarity with the new data, so as to obtain a first updated data subset.) replacing the identified one or more proxy elements and one or more identity elements in the tuning data with masking elements to generate masked tuning data; ([0121] (39) When the maximum similarity is greater than the preset threshold, the client replaces the target data corresponding to the maximum similarity with the new data, so as to obtain a first updated data subset.)
It would have been obvious to a person skilled in the art before the effective filing date of the
invention to have combined with TAN‘s data replacement method and, with GARIMELLA‘s elements, with a reasonable expectation of success, a method that uses correlation scores to determine when to replace data, as in TAN, where the data is elements in a sentence, as found in GARIMELLA. A person of ordinary skill would have been motivated to reduce resource consumption (TAN [0004]).
GARIMELLA further teaches:
and tuning the pre-trained language model with the masked tuning data to generate a tuned language model with reduced bias. ([0061] Debiased language model 108 can be used as an encoder along with debiased transformer-based decoder 120 to form an encoder-decoder summarizer model that can be subjected to fine tuning training 160 using task-specific training corpus 128. Thus in one implementation text generation training module 664 uses debiased language model 108 as an encoder to train debiased transformer-based decoder 120 on task-specific training corpus 128 until losses converge. )
Regarding claim 2, GARIMELLA further teaches:
The method of claim 1, wherein the correlation threshold is determined according to a probability proportional to a bias score. ([0062] The debiasing component is incorporated at inference time during sentence selection, wherein the sentences included in task-specific training corpus 128 are ranked and selected according to a sentence score S that equals the difference between the sigmoid score from the final layer (σ) and the bias score of the sentence (b.sub.s). That is, S=σ−b.sub.s. Here b.sub.s is equal to the constrained co-occurrence score of a given sentence, as provided by Equation (6), below. Sentences are selected for inclusion in debiased extractive summarization 166 that are of high relevance (as reflected by σ) and that contain minimum objectionable or offensive content (as reflected by b.sub.s).
Regarding claims 8-9 and 15-16, they comprise of limitations similar to those of claims 1-2 and are therefore rejected for similar rationale.
Claims 4-5, 11-12, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over GARIMELLA (U.S. Pub. No. US 20220147713 A1), TAN (U.S. Pub. No. US 20180367423 A1) in further view of NELSON (U.S. Pub. No. US 6243713 B1)
The method of claim 1, wherein the at least a portion of elements of the plurality of elements for which respective correlation scores are computed comprises individual tokens of the tuning data. ((Col 22, line 58 – Col 23, line 1) Fine-grain searching 1404 provides more detailed scores of individual token occurrences in the candidate documents. More particularly, fine-grain searching 1406 scores each occurrence of each query token in a candidate document. These occurrences would correspond to "highlights" seen when browsing documents from typical search result sets. Fine-grain searching 1404 compares the reference data of an occurrence as stored in the multimedia index 140 to the reference data of the token in the query to compute a score for each token occurrence. The result is a set 1414 of token occurrences with relevancy scores.)
It would have been obvious to a person skilled in the art before the effective filing date of the
invention to have combined with NELSON‘s tokens and, with GARIMELLA‘s, as modified by TAN, elements, with a reasonable expectation of success, a method for finding a score using tokens, as in NELSON, where the tokens are sentence elements, as found in GARIMELLA, as modified by TAN. A person of ordinary skill would have been motivated to increase efficency (NELSON Col 2, lines 12-16).
NELSON further teaches:
The method of claim 1, wherein the at least a portion of elements of the plurality of elements for which respective correlation scores are computed comprises individual tokens of the tuning data within sentences also including identity elements. ((Col 22, line 58 – Col 23, line 1) Fine-grain searching 1404 provides more detailed scores of individual token occurrences in the candidate documents. More particularly, fine-grain searching 1406 scores each occurrence of each query token in a candidate document. These occurrences would correspond to "highlights" seen when browsing documents from typical search result sets. Fine-grain searching 1404 compares the reference data of an occurrence as stored in the multimedia index 140 to the reference data of the token in the query to compute a score for each token occurrence. The result is a set 1414 of token occurrences with relevancy scores.)
Regarding claims 11-12 and 18-19, they comprise of limitations similar to those of claims 4-5 and are therefore rejected for similar rationale.
Claims 3, 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over GARIMELLA (U.S. Pub. No. US 20220147713 A1), TAN (U.S. Pub. No. US 20180367423 A1) in view of CALIA (U.S. Pub. No. US 5450504 A)
Regarding claim 3, while GARIMELLA teaches claim 1, which claim 3 depends upon, it does not explicitly teach:
The method of claim 1, wherein the correlation threshold of a particular element of the plurality of elements is determined according to a probability proportional to a correlation score of the particular element.
However, in analogous art that similarly teaches finding the correlation between data elements, CALIA teaches:
The method of claim 1, wherein the correlation threshold of a particular element of the plurality of elements is determined according to a probability proportional to a correlation score of the particular element. ((CALIA, claim 22)22. The method of claim 21 comparing;
preselecting a threshold for said correlation score as a function of probability of at least one of the following:
(a) maximum probability of a correct identification
(b) minimum probability of an incorrect identification)
It would have been obvious to a person skilled in the art before the effective filing date of the
invention to have combined with CALIA‘s threshold determination method and, with GARIMELLA‘s, as modified by TAN, elements, with a reasonable expectation of success, a method determining the threshold using probability, as in CALIA, where the probability relates to a data element, as found in GARIMELLA, as modified by TAN. A person of ordinary skill would have been motivated to increase processing speed (CALIA Col. 4, lines 17-32).
Regarding claims 10 and 17, they comprise of limitations similar to those of claim 3 and are therefore rejected for similar rationale.
Claims 6, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over GARIMELLA (U.S. Pub. No. US 20220147713 A1), TAN (U.S. Pub. No. US 20180367423 A1) in view of ZHANG (U.S. Pub. No. US 20050080708 A1)
Regarding claim 6, while GARIMELLA does teach claim 1, which claim 6 depends upon, it does not explicitly teach:
The method of claim 1, wherein computing a correlation score for particular element of the individual elements comprises: computing, for individual elements of the one or more identity elements associated with training bias, respective ratios of respective probabilities of joint distribution with respect to respective probabilities of individual distribution; and assigning a highest ratio of the respective ratios as the correlation score.
However, in analogous art that similarly computes a score for correlation, ZHANG teaches:
The method of claim 1, wherein computing a correlation score for particular element of the individual elements comprises: computing, for individual elements of the one or more identity elements associated with training bias, respective ratios of respective probabilities of joint distribution with respect to respective probabilities of individual distribution; ([0003] In another embodiment, a system comprises a processor and memory containing software executable by the processor. When executing the software, the processor computes a ratio of an estimate of a density function to an estimate of a joint bid distribution, permits a bidder to be selected, obtains a probability value distribution for the selected bidder, and solves an ordinary differential equation. [0028] In block 102, an equation is solved that includes {circumflex over (.psi.)}(b) and the selected bidder's probability value distribution, and not the probability value distribution of other bidders, to compute a bid value associated with the selected bidder for a given bid. In this way, each bidder can be analyzed without having to use the probability value distributions of the other bidders. In accordance with the embodiments of the invention, the equation solved in block 102 comprises equation (2) above.) and assigning a highest ratio of the respective ratios as the correlation score. ([0025] To estimate (b), the n-dimensional data is turned into one-dimensional data by picking the maximum bid in each auction record before applying the kernel method. As such, the dimensionality of the kernel is 1. This results in an estimate of (b) as follows: 7 g ^ ( b ) = 1 N * h g l = 1 N K g ( b - b l , max h g ) ( 8 ))
It would have been obvious to a person skilled in the art before the effective filing date of the
invention to have combined with ZHANG‘s joint distribution ratios and, with GARIMELLA‘s, as modified by TAN, elements, with a reasonable expectation of success, a method for finding a ratio using joint distribution, as in ZHANG, where the data used are sentence elements, as found in GARIMELLA, as modified by TAN. A person of ordinary skill would have been motivated to increase processing speed (ZHANG [0001]).
Regarding claims 13 and 20, they comprise of limitations similar to those of claim 6 and are therefore rejected for similar rationale.
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over GARIMELLA (U.S. Pub. No. US 20220147713 A1), TAN (U.S. Pub. No. US 20180367423 A1) in view of MCNEIL (U.S. Pub. No. US 20210097405 A1)
Regarding claim 7, while GARIMELLA does teach claim 1, which claim 7 depends upon, it does not explicitly teach:
The method of claim 1, further comprising: receiving a dictionary defining the one or more identity elements associated with training bias prior to identifying one or more proxy elements of a plurality of elements in the tuning data that correlate with one or more identity elements associated with training bias; receiving, subsequent to generating the tuned language model, a updated dictionary with a one or more different identity elements associated with training bias,
However, in analogous art that similarly uses a bias, MCNEIL teaches:
The method of claim 1, further comprising: receiving a dictionary defining the one or more identity elements associated with training bias prior to identifying one or more proxy elements of a plurality of elements in the tuning data that correlate with one or more identity elements associated with training bias; ([0059] The bias risk dictionary data structures 114 specify the terms/phrases that may be used as elements of the patterns specified in the rules of the bias risk model.) receiving, subsequent to generating the tuned language model, a updated dictionary with a one or more different identity elements associated with training bias, ([0026] The cognitive computing system is trained through a machine learning process that involves an iterative adjustment of operational parameters of the machine learning computer models employed by the cognitive computing system so as to minimize an error or loss in the outputs or results generated by the cognitive computing system.)
It would have been obvious to a person skilled in the art before the effective filing date of the
invention to have combined with MCNEIL‘s dictionary and, with GARIMELLA‘s, as modified by TAN, elements, with a reasonable expectation of success, a method for making and updating a dictionary, as in MCNEIL, where the data used in the dictionary are sentence elements, as found in GARIMELLA, as modified by TAN. A person of ordinary skill would have been motivated to better manage data (MCNEIL [0002]).
GARIMELLA further teaches:
responsive to receiving the updated dictionary: generating updated masked tuning data; ([0037] During equalization training 104, pretrained language model 100 is further trained on small training corpus 126. More specifically, given a sequence of input words (also referred to as “tokens”) from small training corpus 126, pretrained language model 100 will randomly mask a certain percentage (for example, 15%) of the tokens and learn to predict the masked tokens based on context to the left and right of each masked token. The MLM cross-entropy loss function for predicting the masked tokens in pretrained language model 100 can be expressed as US 20220147713 A1 Garimella) and generating an updated tuned language model with reduced bias using the updated masked tuning data. ([0037] During equalization training 104, pretrained language model 100 is further trained on small training corpus 126. More specifically, given a sequence of input words (also referred to as “tokens”) from small training corpus 126, pretrained language model 100 will randomly mask a certain percentage (for example, 15%) of the tokens and learn to predict the masked tokens based on context to the left and right of each masked token. The MLM cross-entropy loss function for predicting the masked tokens in pretrained language model 100 can be expressed as)
Regarding claim 14, it comprises of limitations similar to those of claim 7 and is therefore rejected for similar rationale.
Response to Arguments
Applicant’s arguments filed 02-FEBURARY-2026 have been fully considered, and have been found to be, in part, persuasive
With regards to the applicant’s remarks regarding the 101 rejection towards an abstract idea, the applicant argues that the claim 1 is not rejectable under U.S.C. 101
Like the claims at issue in Desjardins, Applicant's claims recite features that constitute an improvement to tuning a machine learning model to reduce bias. Therefore, Applicant's claim as a whole integrates the alleged judicial exception into a practical application such that the claim is not directed to the alleged judicial exception. Id. at paragraph [0020]. These improvements are achieved through the claimed technical features as recited. Therefore, Applicant's claim as a whole integrates the alleged judicial exception into a practical application such that the claim is not directed to the alleged judicial exception.
With regards to this argument, according to MPEP 2106(a)(II), the claims themselves have to show the improvement as well: “To show that the involvement of a computer assists in improving the technology, the claims must recite the details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology. “ The improvement cannot simply lay in the abstract idea itself. As such, the abstract ideas of selecting data and identifying data must become a part of the practical application. Therefore, the examiner maintains the 101 rejection set forth.
With regards to the applicant’s remarks regarding the 102 rejection in the non-final action, the applicant argues that the prior art PANDA is not admissible prior art due to being owned by the same person or being subject to an obligation of assignment to the same person. The examiner acknowledges and agrees with the argument presented and to compensate for the insufficient prior art of PANDA has deemed it necessary to respond to said argument with a secondary non-final office action. As such, new prior arts GIRAMELLA, TAN, and NELSON have been introduced to be mapped to the original claim set.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SKIELER A KOWALIK whose telephone number is (571)272-1850. The examiner can normally be reached 8-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mariela D Reyes can be reached at (571)270-1006. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SKIELER ALEXANDER KOWALIK/Examiner, Art Unit 2142 /Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142
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