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 without significantly more.
Step One
Claims 1 – 7 are directed to a method.
Claims 15 - 20 are directed to a system with structural components.
Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Claims 8 - 14 recites “one or more computer-readable storage mediums” that stores a software program performing a function. The Specification fails to expressly limit the recited “mediums” to a statutory embodiment. Thus, the plain and ordinary meaning of the recited "mediums" includes signals, carrier waves, etc. Accordingly, the recited “one or more computer-readable storage mediums” are not a process, a machine, a manufacture or a composition of matter, and Claims 9-16 fail to recite statutory subject matter as defined in 35 U.S.C. 101.
As to claim 1,
Step 2A, Prong One
The claim recites in part:
measuring, by said quantum computer, a quantum state of said quantum model by employing a quantum circuit to obtain a collection of observed bitstrings;
For example, a person can observe and record measurement results.
calculating, by a classical computer, index values based on said collection of observed bitstrings;
For example, a person can assign values to the observed results.
calculating, by said classical computer, said indicator value based on said index values and said collection of observed bitstrings;
For example, a person can evaluate the results using those assigned values.
identifying, by said quantum computer, a data point that optimizes said indicator value;
For example, a person can determine which candidate gives the best result..
updating, by said classical computer, said dataset with said identified data point.
For example, a person adding the selected candidate to a record.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
training, on a quantum computer, a quantum model with said dataset;
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The quantum computer, quantum circuit, and classical computer, are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The recitation of quantum state and quantum model amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional elements of:
training, on a quantum computer, a quantum model with said dataset;
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The quantum computer, quantum circuit, and classical computer, are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The recitation of quantum state and quantum model amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 2,
Step 2A, Prong One
The claim recites in part:
updating, by said classical computer, parameters of said quantum circuit that optimizes said indicator value
For example, a person altering the parameters that changes the indicator value in a positive way.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 3,
Step 2A, Prong One
The claim recites in part:
wherein said updating of said parameters of said quantum circuit is performed using stochastic methods, wherein said stochastic methods comprise one of the following from the group consisting of Bayesian optimization and covariance matrix adaptation evolution.
For example, a person altering the parameters that changes the indicator value in a positive way.
Under the broadest reasonable interpretation, these limitations are process steps that cover Mathematical Concepts. If a claim, under its broadest reasonable interpretation, covers a mathematical concept, then it falls within the “Mathematical Concepts” grouping of abstract ideas.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 4,
Step 2A, Prong One
The claim recites in part:
identifying, by said quantum computer, said data point that optimizes said indicator value
For example, a person can determine which candidate gives the best result.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to the judicial exception.
As to claim 5,
Step 2A, Prong One
The claim recites in part:
measuring, by said quantum computer, a value of an objective variable as a function of binary data that optimizes said indicator value by employing said quantum circuit after updating said parameters of said quantum circuit, wherein said objective variable and said binary data collectively form said identified data point.
For example, a person can observe and record measurement results.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 6,
Step 2A, Prong One
The claim recites in part:
adjusting a best indicator value within said dataset identified data point.
For example, a person can observe and adjust values as they see
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 7,
Step 2A, Prong One
The claim recites the abstract idea described above in claim 1, but does not recite any other abstract ideas or any other judicial exceptions.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein said quantum model is constructed based on machine learning models
which is recited at a high-level of generality with no detail of the construction process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional elements of:
wherein said quantum model is constructed based on machine learning models
which is recited at a high-level of generality with no detail of the constrcution process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 8 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
The computer program product, one or more computer readable storage mediums, a quantum computer, quantum circuit, and classical computer are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Claim 9 has similar limitations as claim 2. Therefore, the claim is rejected for the same reasons as above.
Claim 10 has similar limitations as claim 3. Therefore, the claim is rejected for the same reasons as above.
Claim 11 has similar limitations as claim 4. Therefore, the claim is rejected for the same reasons as above.
Claim 12 has similar limitations as claim 5. Therefore, the claim is rejected for the same reasons as above.
Claim 13 has similar limitations as claim 6. Therefore, the claim is rejected for the same reasons as above.
Claim 14 has similar limitations as claim 7. Therefore, the claim is rejected for the same reasons as above.
Claim 15 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
The system, a memory, a processor, a quantum computer, quantum circuit, and classical computer are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Claim 16 has similar limitations as claim 2. Therefore, the claim is rejected for the same reasons as above.
Claim 17 has similar limitations as claim 3. Therefore, the claim is rejected for the same reasons as above.
Claim 18 has similar limitations as claim 4. Therefore, the claim is rejected for the same reasons as above.
Claim 19 has similar limitations as claim 5. Therefore, the claim is rejected for the same reasons as above.
Claim 20 has similar limitations as claim 6. Therefore, the claim is rejected for the same reasons as above.
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.
Claim 1, 8, and 15 recites the limitation "with said dataset," and there is insufficient antecedent basis for this limitation in the claim. It seems as if the applicant uses “limited dataset” and “dataset” interchangeably but in reality, a limited dataset and a dataset are two separate and distinct elements of the claim.
Claims 2 -7, 9 – 14, and 16 – 20 depend on claims 1, 8, and 15, respectively, therefore the claims are also rejected.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1 – 20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Le Van Gong et al (US 2023/0206108).
As to claim 1, Van Gong et al figures 1 - 4 shows and teaches a method for augmenting a limited dataset with data to optimize an indicator value (ABSTRACT teaches Quantum computers with a limited number of input qubits are used to perform machine learning processes having a far greater number of trainable features), the method comprising:
training, on a quantum computer, a quantum model with said dataset (paragraph [0013]…FIG. 1 is a block diagram of a quantum computer machine learning model 10 according to one non-limiting embodiment. The quantum computer machine learning model 10 is configured to receive a set of trainable features as its input and use an iterative process to identify the features that are more relevant than the rest of the features in performing a predefined task or achieving a predefined objective...By training the quantum computer machine learning model 10, the more relevant features may be identified or obtained as an output ; paragraph [0042]…the system 100 may further include a quantum computer machine learning server 200)(Examiner’s Note: “quantum computer machine learning model 10” reads on “a quantum computer” ; “quantum computer machine learning model 10 is configured to receive a set of trainable features as its input and use an iterative process to identify the features that are more relevant than the rest of the features” reads on “a quantum model with said dataset”);
measuring, by said quantum computer, a quantum state of said quantum model by employing a quantum circuit to obtain a collection of observed bitstrings (paragraph [0014]…the parameterized quantum circuit 12 itself includes a variational training circuit and a measurement circuit. The variational training circuit includes an alternating layer of trainable single qubit gates R.sub.y(θ) to receive inputs q.sub.0, q.sub.1, q.sub.2, q.sub.3, and q.sub.4 for the quantum computer machine learning model 10, which may be the trainable features of a machine learning process discussed above ; paragraph [0016]…the results of the variational training circuit are measured by the measurement circuit. In some embodiments, such as in superconducting and ion traps based quantum computers, the measurement circuit measures the results of the variational training circuit by measuring the electromagnetic frequency associated with the qubit gates. For example, the measurement circuit may apply a laser pulse on the layer of qubit gates, which may include a superconducting material. The application of the laser pulse may affect the state of the qubit (e.g., a state of 0 or 1), which affects the electromagnetic frequency associated with the qubit. As such, the measured electromagnetic frequency of the qubit may indicate the state the qubit is in)(Examiner’s Note: “the measurement circuit measures the results of the variational training circuit by measuring the electromagnetic frequency associated with the qubit gates…, the measured electromagnetic frequency of the qubit may indicate the state the qubit is in” reads on “measuring, by said quantum computer, a quantum state of said quantum model by employing a quantum circuit to obtain a collection of observed bitstrings” ; “the measurement circuit” reads on “a quantum circuit” ; “a state of 0 or 1” reads on “”bitstring”);
calculating, by a classical computer, index values based on said collection of observed bitstrings (paragraph [0021]… First, a plurality of features (labeled in FIG. 2 as x.sub.1, x.sub.2 . . . x.sub.n) are received, for example, by a feature grouping module 30 (which may be implemented using a non-quantum computer (e.g., a classical computer with standard 0 and 1 bits) with hardware components running specialized designed software code ; paragraph [0022]..the plurality of features x.sub.1, x.sub.2 . . . x.sub.n is divided or grouped, by the feature grouping module 30, into a plurality feature groups (labeled as G.sub.1, G.sub.2 . . . G.sub.k herein) as a first step of the machine learning process… The features may be grouped into the feature groups using a variety of techniques, for example, using principal component analysis, random grouping, or sequential grouping, etc.)(Examiner’s Note: “the plurality of features x.sub.1, x.sub.2 . . . x.sub.n is divided or grouped, by the feature grouping module 30, into a plurality feature groups (labeled as G.sub.1, G.sub.2 . . . G.sub.k herein)” reads on “calculating, by a classical computer, index values based on said collection of observed bitstrings”);
calculating, by said classical computer, said indicator value based on said index values and said collection of observed bitstrings (paragraph [0023]…a binary variable may be assigned for each of the feature groups. If the value of the binary variable is 1, the corresponding feature group is included for the subsequent training using a quantum computer machine learning model 10A, which is a first instance of quantum computer machine learning model 10 discussed above with reference to FIG. 1. If the value of the binary variable is 0, the corresponding feature group is not included for the subsequent training using the quantum computer machine learning model 10A)(Examiner’s Note: “a binary variable may be assigned for each of the feature groups” reads on “calculating, by said classical computer, said indicator value based on said index values and said collection of observed bitstrings”);
identifying, by said quantum computer, a data point that optimizes said indicator value (paragraph [0024]…After the quantum computer machine learning model 10A is trained, it generates an output that is an optimal vector X∈{0, 1}.sup.n. This vector lists the feature groups 60-61 that are selected as well as feature groups 70 that are unselected or rejected by the model, where the selected feature groups are determined or identifies as being more relevant than the unselected or rejected feature groups in performing the predefined task (e.g., determining fraud in transactions))(Examiner’s Note: “After the quantum computer machine learning model 10A is trained, it generates an output that is an optimal vector X∈{0, 1}.sup. n” reads on” “identifying, by said quantum computer, a data point that optimizes said indicator value”); and
updating, by said classical computer, said dataset with said identified data point (paragraph [0026]… the machine learning process herein will also use feedback to update the input features lists. This may be done in two stages. The first stage of the feedback is done at the output of the quantum computer machine learning model 10A, where the unselected feature groups 70 are fed back to the beginning (e.g., to the feature grouping module 30) of the machine learning process, rather than being discarded. This is because each feature group (whether selected or unselected) contains a plurality of features, and some of these features may still have high relevancy (e.g., with respect to detecting fraud), but the high irrelevancy of the rest of the features in that feature group resulted in that feature group being unselected. Thus, to prevent the potentially high relevancy features from being omitted, these unselected feature groups 70 (or the features contained therein) are fed back to the beginning of the process, where these features are then re-mixed by the feature grouping module 30 to generate new feature groups. By doing this process iteratively, eventually all the high relevancy features should be discovered)(Examiner’s Note: “the machine learning process herein will also use feedback to update the input features lists” reads on “updating, by said classical computer, said dataset with said identified data point”).
As to claim 2, Van Gong et al figures 1 - 4 shows and teaches the method
further comprising: updating, by said classical computer, parameters of said quantum circuit that optimizes said indicator value (paragraph [0013]…update the parameters of the quantum circuit using the classical optimization feedback).
As to claim 3, Van Gong et al figures 1 - 4 shows and teaches the method, wherein said updating of said parameters of said quantum circuit is performed using stochastic methods, wherein said stochastic methods comprise one of the following from the group consisting of Bayesian optimization and covariance matrix adaptation evolution (paragraph [0074]…Bayesian networks may be used to implement machine learning. A Bayesian network is an acyclic probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). The Bayesian network could present the probabilistic relationship between one variable and another variable. Other types of machine learning algorithms are not discussed in detail herein for reasons of simplicity).
As to claim 4, Van Gong et al figures 1 - 4 shows and teaches the method further comprising: identifying, by said quantum computer, said data point that optimizes said indicator value by employing said quantum circuit after updating said parameters of said quantum circuit (paragraph [0013]…As shown in FIG. 1, the quantum computer machine learning model 10 includes a parameterized quantum circuit 12 and an optimization circuit 14 coupled to the parameterized quantum circuit 12. At a high level, the quantum-classical optimization in FIG. 1 is performed based on the following steps: 1. Initialize the trainable parameters of the quantum circuit randomly. 2. Use the measurement of the quantum circuit to generate classical samples. 3. Feed the classical samples into a standard classical optimization routine. 4. Update the parameters of the quantum circuit using the classical optimization feedback.5. Iteratively keep performing the procedure until a certain target level is reached)(Examiner’s Note: “Update the parameters of the quantum circuit using the classical optimization feedback.5. Iteratively keep performing the procedure until a certain target level is reached” reads on “identifying, by said quantum computer, said data point that optimizes said indicator value by employing said quantum circuit after updating said parameters of said quantum circuit”).
As to claim 5, Van Gong et al figures 1 - 4 shows and teaches the method further comprising: measuring, by said quantum computer, a value of an objective variable as a function of binary data that optimizes said indicator value by employing said quantum circuit after updating said parameters of said quantum circuit, wherein said objective variable and said binary data collectively form said identified data point (paragraph [0020]…The training of the quantum circuit includes multiple iterations. At each iteration, the quantum state |ϕ(θ)custom-character=∪(θ)|)custom-character arising from the variational training circuit is measured in the computational basis to obtain bit strings f.sub.1, f.sub.2, . . . , f.sub.k∈{0, 1}.sup.5. The feature sets output by the quantum computer machine learning model 10 at any given iteration is then the number of non-zero elements in the bit strings. Repeated sampling of the bit strings also allows an estimation of the respective probability of the obtained samples: p.sub.1(θ), p.sub.2(θ), p.sub.k(θ). For each of the obtained samples, logistic regression may be used to fit it with regards to the training data and compute the negative log-loss score with regards to test data. In some embodiments, the scores of the samples may be computed with logistic regression as scores s.sub.1, s.sub.2, . . . s.sub.k. The scores then determine the loss function L, which is then minimized, for example, using a gradient descent method, to update the parameters of the variational training circuit)(Examiner’s Note: “The scores then determine the loss function L, which is then minimized, for example, using a gradient descent method, to update the parameters of the variational training circuit” reads on “a value of an objective variable as a function of binary data that optimizes said indicator value by employing said quantum circuit”).
As to claim 6, Van Gong et al figures 1 - 4 shows and teaches the method
further comprising: adjusting a best indicator value within said dataset with said identified data point (paragraph [0024]…After the quantum computer machine learning model 10A is trained, it generates an output that is an optimal vector X∈{0, 1}.sup.n. This vector lists the feature groups 60-61 that are selected as well as feature groups 70 that are unselected or rejected by the model, where the selected feature groups are determined or identifies as being more relevant than the unselected or rejected feature groups in performing the predefined task (e.g., determining fraud in transactions))(Examiner’s Note: “unselected or rejected feature groups” reads on “adjusting a best indicator value”).
As to claim 7, Van Gong et al figures 1 - 4 shows and teaches the method
wherein said quantum model is constructed based on machine learning models (paragraph [0013]…FIG. 1 is a block diagram of a quantum computer machine learning model 10 according to one non-limiting embodiment. The quantum computer machine learning model 10 is configured to receive a set of trainable features as its input and use an iterative process to identify the features that are more relevant than the rest of the features in performing a predefined task or achieving a predefined objective).
Claim 8 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
Claim 9 has similar limitations as claim 2. Therefore, the claim is rejected for the same reasons as above.
Claim 10 has similar limitations as claim 3. Therefore, the claim is rejected for the same reasons as above.
Claim 11 has similar limitations as claim 4. Therefore, the claim is rejected for the same reasons as above.
Claim 12 has similar limitations as claim 5. Therefore, the claim is rejected for the same reasons as above.
Claim 13 has similar limitations as claim 6. Therefore, the claim is rejected for the same reasons as above.
Claim 14 has similar limitations as claim 7. Therefore, the claim is rejected for the same reasons as above.
Claim 15 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
Claim 16 has similar limitations as claim 2. Therefore, the claim is rejected for the same reasons as above.
Claim 17 has similar limitations as claim 3. Therefore, the claim is rejected for the same reasons as above.
Claim 18 has similar limitations as claim 4. Therefore, the claim is rejected for the same reasons as above.
Claim 19 has similar limitations as claim 5. Therefore, the claim is rejected for the same reasons as above.
Claim 20 has similar limitations as claim 6. Therefore, the claim is rejected for the same reasons as above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON S COLE whose telephone number is (571)270-5075. The examiner can normally be reached Mon - Fri 7:30pm - 5pm EST (Alternate Friday's Off).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez can be reached at 571-272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRANDON S COLE/ Primary Examiner, Art Unit 2128
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