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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. KR10-2023-0154660, filed on 11/09/2023.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitations uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are:
“a first generation device configured to” in claims 1, 3.
“a second generation device configured to” in claims 1 and 4.
“a determination device configured to” in claims 1 and 5.
“a quality prediction model construction device configured to” in claim 2.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitations to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
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 1-20 are 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.
Claims 1, 7, and 13 recites “determine a third generative model among the first generative model and the second generative model”. It is not clear what constitutes as determining a third generative model among the first and second generative model. In one interpretation, a third generative model implies that it is a different model and the first and second model may be combined to generate a new third model. In a different interpretation, one of the first model or the second model is selected to be designated as a third model. The claims should be amended to clarify how the determining step is executed and/or what constitutes as determining a third generative model. Examiner interprets the claim as selecting among the first or second generative model to determine the third generative model.
Claims 1, 7, and 13 recites “wherein the third generative model is a result of an evaluation of the plurality of first process factors and the plurality of second process factors using the quality prediction model”. It is not clear how a model can be a result based on the evaluation of 2 datasets. The claims should be amended to clarify how the evaluation of datasets by a quality prediction model produces a third generative model. Examiner interprets the claim as using a quality prediction model to evaluate the generated datasets by different generative models based on a metric and selecting one of the generative models as a third generative model to perform tasks.
Claims 2-6, 8-12, and 14-20 are dependent claims of claims 1, 7, and 13. Therefore, the dependent claims are rejected on the same basis as the parent claims.
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 without significantly more.
Subject Matter Eligibility Analysis Step 1:
Claims 1-6 recite a machine (“A system for designing a process factor”), one of the four statutory categories of patentable subject matter. Claims 7-12 recite a process (“A method for designing a process factor of a system”), one of the four statutory categories of patentable subject matter. Claims 13-20 recite a process (“A method for manufacturing a system for designing a process factor”), one of the four statutory categories of patentable subject matter.
Regarding Claim 1:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement)
“” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement)
“automatically generate at least one process factor applied to a production process ” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement)
Claim 1 therefore recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
“a first generation device configured to ” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
“a second generation device configured to ” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
“a determination device configured to ” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
“wherein the quality prediction model is an artificial neural network model that receives a process factor of the production process as input data and predicts quality data based on the input data” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
“wherein the third generative model is a result of an evaluation of the plurality of first process factors and the plurality of second process factors using the quality prediction model” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above. Therefore, Claim 1 is directed to the abstract idea.
Subject Matter Eligibility Analysis Step 2B:
“a first generation device configured to ” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
“a second generation device configured to ” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
“a determination device configured to ” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
“wherein the quality prediction model is an artificial neural network model that receives a process factor of the production process as input data and predicts quality data based on the input data” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
“wherein the third generative model is a result of an evaluation of the plurality of first process factors and the plurality of second process factors using the quality prediction model” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
The additional elements as disclosed above alone or in combination do not recite significantly more than the abstract idea itself as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above. Therefore, Claim 1 is subject-matter ineligible.
Regarding Claim 7:
The claim recites a process that performs the method of the system described in claim 1. Therefore, claim 7 is rejected for the same reasons as disclosed in claim 1.
Regarding Claim 13:
The claim recites a process that performs the method of the system described in claim 1. Therefore, claim 13 is rejected for the same reasons as disclosed in claim 1.
Regarding Claims 2, 8, and 14:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“a quality prediction model construction device configured to construct the quality prediction model by learning the artificial neural network model using a plurality of third process factors and a plurality of quality data collected from the production process as learning data” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claims 3, 9, and 15:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the first generation device is further configured to ” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claims 4, 10, and 16:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the second generation device is further configured to use the quality prediction model as a discriminative model of the second generative model to learn the second generative model” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claims 5, 11, 17, 18 and 19:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“analyze output data output from the quality prediction model to calculate a first ratio and a second ratio” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement)
“determine the optimal generative model based on a comparison result of the first ratio and the second ratio” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“input the plurality of first process factors and the plurality of second process factors as the input data of the quality prediction model” (This step is directed to data gathering, which is understood to be insignificant extra solution activity (2106.05(g) in step 2A prong 2) and well understood, routine and conventional activity of transmitting and receiving data as identified by the court (2106.05(d) in step 2B))
“the first ratio is a ratio of process factors satisfying a target quality among the plurality of first process factors, and the second ratio is a ratio of process factors satisfying a target quality among the plurality of second process factors” (merely specifies a particular technological environment in which the abstract idea is to take place, ie. a field of use, and thus does not integrate the abstract idea into a practical application nor cannot provide significantly more than the abstract idea itself - see MPEP 2106.05(h))
Regarding Claims 6, 12, and 20:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“the production process is a production process of a battery; the at least one process factor applied to the production process is a factor representing a design condition in the production process of the battery and comprises at least one of a charging voltage, a charging capacity, a charging time, a charging cell temperature, a discharging voltage, a discharging current, a discharging capacity, a discharging cell temperature, and an electrolyte weight; and the quality data indicates whether a target quality for at least one quality factor indicating a quality of a battery is achieved” (merely specifies a particular technological environment in which the abstract idea is to take place, ie. a field of use, and thus does not integrate the abstract idea into a practical application nor cannot provide significantly more than the abstract idea itself - see MPEP 2106.05(h))
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 1-5, 7-11, and 13-19 are rejected under 35 U.S.C. 103 as being unpatentable over Stein (US20240419978A1) in view of Duquesnoy “Machine Learning-Assisted Multi-Objective Optimization Of Battery Manufacturing From Synthetic Data Generated By Physics-Based Simulations” and Tian “Knowledge Representation of Training Data With Adversarial Examples Supporting Decision Boundary”.
Regarding claim 1, Stein teaches:
“A system for designing a process factor, comprising” (abstract, A plurality of generative model is trained on a reference data set to generate additional data samples. The generated data is evaluated based on various candidate metrics to measure the generative model’s fidelity.)
“a first generation device configured to generate a plurality of first process factors using a first generative model ” ([0023-0024, 0030, 0040, Figure 2], A plurality of generative model is employed to generate samples from a reference data set. Under the broadest reasonable interpretation, process factors are directed to any data that influences an analytical, operational or manufacturing procedure and image data acts as a variable factor in image processing tasks. In one embodiment, the first generative model may be a diffusion model that generates synthetic image samples. An encoder model or a classifier may be used to evaluate the plurality of generative models.)
“a second generation device configured to generate a plurality of second process factors using a deep learning-based second generative model” ([0030, 0040, Figure 2], In one embodiment, a second generative model may be a generative adversarial network model.)
“a determination device configured to determine a third generative model among the first generative model and the second generative model and automatically generate at least one process factor ” ([0029, 0032-0034, 0039], The generated data set for each generative model is evaluated. In one embodiment, the same candidate encoding model may be used with different candidate metrics for evaluating the generative models. The performance of the generative model is determined, and the preferred model (third generative model) may be selected among the plurality of models. The selected model may be used for further downstream tasks or used for additional training. When the selected generative model is used for a particular task, it is implied that the model produces samples as an output.)
“wherein the quality prediction model is an artificial neural network model that receives a process factor ” ([0023-0024, 0033], The encoder model is used to evaluate the generative models and the encoder model may be a autoencoder. The encoder model receives the generated data set from the generative model and generates representations of the generated data and the reference data. A candidate scoring is applied to the representations and the scoring may be used to rank the generated data set.)
“wherein the third generative model is a result of an evaluation of the plurality of first process factors and the plurality of second process factors using the quality prediction model” ([0029, 0032-0034, 0039], The generated data set for each generative model is evaluated. In one embodiment, the same candidate encoding model may be used with different candidate metrics for evaluating the generative models. The performance of the generative model is determined, and the preferred model may be selected among the plurality of models.)
Stein does not explicitly disclose an implementation of “… a first generative model capable of estimating a decision boundary of a quality prediction model …”, “… automatically generate at least one process factor applied to a production process using the third generative model”, and “… a process factor of the production process …”. However, Duquesnoy discloses in the same field of endeavor:
“… automatically generate at least one process factor applied to a production process using the ” ([pg. 2-3, Section 1, par. 4; pg. 4, Section 1.1, par. 1-2], A synthetic dataset consisting of manufacturing conditions is generated for the production process of a battery. The manufacturing conditions include amount of active material, slurry solid content and the calendaring compression rate. A multi-objective optimization is also applied to determine optimal electrode properties.)
“wherein the quality prediction model ” ([pg. 2-3, Section 1, par. 4; pg. 5, Section 1.3, par. 1-2; pg. 5, Section 1.4, par. 1], A surrogate model is used to directly calculate the electrode properties as a function of the manufacturing parameters. An optimization process is used to determine the best set of manufacturing conditions with resulting optimal electrode properties.)
It would be obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of “… automatically generate at least one process factor applied to a production process using the third generative model”, and “… a process factor of the production process …” from Duquesnoy into the teaching of Stein. The generative framework from Stein is incorporated into Duquesnoy to produce synthetic data for battery manufacturing process. Doing so can improve the optimization of the electrodes manufacturing process by generating a synthetic dataset that represents the manufacturing parameters space (Duquesnoy, abstract).
Stein in view of Duquesnoy does not explicitly disclose an implementation of “… a first generative model capable of estimating a decision boundary of a quality prediction model …”. However, Tian discloses in the same field of endeavor:
“… a first generative model capable of estimating a decision boundary of a quality prediction model …” ([pg. 4, Section B.1, par. 1-2; pg. 4, Figure 2], A adversarial example is generated based on an adversarial attack against a model. Figure 2 shows the adversarial example from the targeted sample.)
It would be obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of “… a first generative model capable of estimating a decision boundary of a quality prediction model …” from Tian into the teaching of Stein in view of Duquesnoy. Doing so can improve the performance of deep learning models by implementing a framework to generate additional samples supporting the decision boundary of a model (Tian, abstract).
Regarding claim 7:
Claim 7 recites a process that performs the same method as described in Claim 1. Therefore claim 7 is rejected under the same reasons mentioned for claim 1.
Regarding claim 13:
Claim 13 recites a process that performs the same method as described in Claim 1. Therefore claim 13 is rejected under the same reasons mentioned for claim 1.
Regarding claims 2, 8, and 14 Stein teaches:
“a quality prediction model construction device configured to construct the quality prediction model by learning the artificial neural network model using a plurality of third process factors and a plurality of quality data collected from the production process as learning data” ([0023-0024, 0033], The encoder model receives the generated data set from the generative model and generates representations of the generated data and the reference data. An encoder model is trained to learn a representation that may be used to recreate the original data sample. The training may consist of processing the generated data samples and computing performance metrics of the model.)
Regarding claims 3, 9, and 15 Stein in view of Duquesnoy and Tian teaches:
“wherein the first generation device is further configured to estimate the decision boundary by applying an adversarial attack on the quality prediction model” ([Tian, pg. 4, Section B.1, par. 1-2; pg. 4, Figure 2], An adversarial example is generated based on an adversarial attack against a model.)
Regarding claims 4, 10, and 16 Stein teaches:
“wherein the second generation device is further configured to use the quality prediction model as a discriminative model of the second generative model to learn the second generative model” ([0023-0024, 0033], The encoder model receives the generated data set from the generative model and generates representations of the generated data and the reference data. An encoder model is trained to learn a representation that may be used to recreate the original data sample. The evaluation consists of parallel examination of a variety of representation spaces. In some embodiment, classifiers can be used in place of encoder models.)
Regarding claims 5, 11, 17, 18, and 19 Stein teaches:
“input the plurality of first process factors and the plurality of second process factors as the input data of the quality prediction model” ([0033-0034, Figure 2], The generated data from each of the generative model is provided to an encoder model. In one embodiment, the same candidate encoding model may be used with different candidate metrics for evaluating the generative models.)
“analyze output data output from the quality prediction model to calculate a first ratio and a second ratio” ([0026, 0033, 0040, Figure 2], After processing by an encoder, a particular candidate metric evaluates a scoring function within the representation space. The scoring function may be feature likelihood score, which measures how realistic the generated samples are compared to the real data.)
“determine the optimal generative model based on a comparison result of the first ratio and the second ratio” ([0026, 0039], The calculated score determines the performance of the generative model and can be used to select an optimal model based on the computed scores.)
“the first ratio is a ratio of process factors satisfying a target quality among the plurality of first process factors, and the second ratio is a ratio of process factors satisfying a target quality among the plurality of second process factors” ([0026, 0033, 0040, Figure 2], After processing by an encoder, a particular candidate metric evaluates a scoring function within the representation space. In one embodiment, the scoring function is feature likelihood score, which measures how realistic the generated samples are compared to the real data. The goal is to generate synthetic data that closely resembles real data.)
Claims 6, 12, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Stein (US20240419978A1) in view of Duquesnoy “Machine Learning-Assisted Multi-Objective Optimization Of Battery Manufacturing From Synthetic Data Generated By Physics-Based Simulations”, Tian “Knowledge Representation of Training Data With Adversarial Examples Supporting Decision Boundary” and Pyne, “Synthetic Battery Data Generation and Validation for Capacity Estimation”.
Regarding claims 6, 12, and 20, Stein in view of Duquesnoy and Tian teaches:
“the production process is a production process of a battery” ([Duquesnoy, abstract], The framework describes the manufacturing process of a battery and generating a synthetic dataset for manufacturing a battery.)
“the at least one process factor applied to the production process is a factor representing a design condition in the production process of the battery ” ([Duquesnoy, pg. 2-3, Section 1, par. 4; pg. 4, Section 1.1, par. 1-2], A synthetic dataset consisting of manufacturing conditions is generated for the production process of a battery. The manufacturing conditions include amount of active material, slurry solid content and the calendaring compression rate. These manufacturing conditions indirectly relates to the charging capacity and current of a battery.)
“the quality data indicates whether a target quality for at least one quality factor indicating a quality of a battery is achieved” ([Duquesnoy, pg. 2-3, Section 1, par. 4; pg. 5, Section 1.3, par. 1-2; pg. 5, Section 1.4, par. 1], A surrogate model is used to directly calculate the electrode properties as a function of the manufacturing parameters. An optimization process is used to determine the best set of manufacturing conditions with resulting optimal electrode properties.)
Stein in view of Duquesnoy and Tian does not explicitly disclose an implementation of “… comprises at least one of a charging voltage, a charging capacity, a charging time, a charging cell temperature, a discharging voltage, a discharging current, a discharging capacity, a discharging cell temperature, and an electrolyte weight”. However, Pyne discloses in the same field of endeavor:
“the at least one process factor applied to the production process is a factor representing a design condition in the production process of the battery and comprises at least one of a charging voltage, a charging capacity, a charging time, a charging cell temperature, a discharging voltage, a discharging current, a discharging capacity, a discharging cell temperature, and an electrolyte weight” ([pg. 3, Section 2, par. 1-2; pg. 4, Section 2.1.2, par. 1-3], The framework describes generating a synthetic dataset for testing of a battery. Under the broadest reasonable interpretation, testing can be part of the final stage of the production process of a battery. The synthetic dataset consists of synthetic current profiles for battery testing and modeling. A Markov chain approach is used to model the charge and discharge pulses of a battery pack.)
It would be obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of “… comprises at least one of a charging voltage, a charging capacity, a charging time, a charging cell temperature, a discharging voltage, a discharging current, a discharging capacity, a discharging cell temperature, and an electrolyte weight” from Pyne into the teaching of Stein in view of Duquesnoy and Tian. Doing so can improve the analysis of battery systems by implementing a framework to generate synthetic data for prolonged cycling tests of batteries (Pyne, abstract).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GARY MAC whose telephone number is (703)756-1517. The examiner can normally be reached Monday - Friday 8:00 AM - 5:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abdullah Kawsar can be reached at (571) 270-3169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/GARY MAC/Examiner, Art Unit 2127
/ABDULLAH AL KAWSAR/Supervisory Patent Examiner, Art Unit 2127
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