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 .
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
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 - 13 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.
As to claims 1, 8, and 13, the limitations “learn parameters of predetermined multiple operations that are related in that common input data is given and that weighted sum of output data is calculated, and parameters related to calculation of the weighted sum” are not understood by the examiner, as it is not exactly clear what the applicant is trying to claim. The limitations has grammatical errors which makes it impossible for the examiner to accurate map. The examiner will interpret the claims as if the learn parameters are associated with common input data and the calculation of the weighted sum.
Claims 2 - 7 and 9 -12 depend on claims 1 and 8, respectively, and are also rejected.
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 – 13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step One
The claims are directed to a learning system with structural components (claims 1 - 7), a learning method (claims 8 - 11), and a non-transitory computer-readable recording medium with structural components (claim 13). Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
As to claim 1,
Step 2A, Prong One
The claim recites in part:
learn parameters of predetermined multiple operations that are related in that common input data is given and that weighted sum of output data
is calculated, and parameters related to calculation of the weighted sum;
For example, a human mentally learns the parameters for multiple operations, applies them to the same input, and mentally calculates a weighted sum of the outputs.
recalculate the parameters of the predetermined multiple operations, based on the parameters of the predetermined multiple operations received from each client; and
For example, a human mentally learns the updated parameters for multiple operations, applies them to the same (or another) input, and mentally recalculates a weighted sum of the outputs.
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:
send the parameters of the predetermined multiple operations, among the parameters of the predetermined multiple operations and the parameters related to the calculation of the weighted sum, to the server;
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
The claim further recites a learning system, a server, multiple clients, memories, and processors which 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)).
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 that the judicial exception. As discussed above, the additional elements of:
send the parameters of the predetermined multiple operations, among the parameters of the predetermined multiple operations and the parameters related to the calculation of the weighted sum, to the server;
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
The claim further recites a learning system, a server, multiple clients, memories, and processors which 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)).
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 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.
The claim recites “the first processor of each client learns the parameters related to the calculation of the weighted sum independently” which 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)).
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 that the judicial exception. :
The claim recites “the first processor of each client learns the parameters related to the calculation of the weighted sum independently” which 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)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 3,
Step 2A, Prong One
The claim recites in part:
wherein the number of the predetermined multiple operations is lower than the number of the multiple clients
For example, a human mentally compare the number of the predetermined multiple operations to the number of the multiple clients to determine which number is lower.
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.
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:
wherein the predetermined multiple operations are all linear operations
For example, a human mentally decide that the predetermined multiple operations are linear operations
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.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 5,
Step 2A, Prong One
The claim recites in part:
wherein the first processor of each client, when the parameters of the predetermined multiple operations and the parameters related to the calculation of the weighted sum are determined, converts the predetermined multiple operations into a single operation based on the parameters of the predetermined multiple operations and the parameters related to the calculation of the weighted sum.
For example, a human mentally learns the parameters for multiple operations, applies them to the same input, and mentally calculates a weighted sum of the outputs.
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.
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:
wherein the first processor of each client, when the parameters of the predetermined multiple operations and the parameters related to the calculation of the weighted sum are determined, derives an inference result for given data based on a model determined by the parameters of the predetermined multiple operations and the parameters related to the calculation of the weighted sum
.For example, a human mentally learns the parameters for multiple operations, applies them to the same input, and mentally calculates a weighted sum of the outputs.
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.
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 in part:
derive an inference result for given data based on a model determined by the parameters of the predetermined multiple operations and the
parameters related to the calculation of the weighted sum that are obtained by the learning system
.For example, a human mentally learns the parameters for multiple operations, applies them to the same input, and mentally calculates a weighted sum of the outputs to derive an inference 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 judicial exception is not integrated into a practical application.
The claim recites memories and processors which 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)).
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 that the judicial exception. :
The claim recites memories and processors which 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)).
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.
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 1. Therefore, the claim is rejected for the same reasons as above.
The claim further recites a non-transitory computer-readable recording medium, a computer, and server which 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 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 - 3, 8 - 10, and 13 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Da Silva et al (US 2021/0383197).
As to claim 1, Da Silva et al figures 1A-1C, 2, and 4 teaches a learning system comprising a server and multiple clients (paragraph [0018]…the system (100) may include, but is not limited to, two or more client nodes (102A-102N) operatively connected to a central node (104) through a network (106) ; paragraph [0019]… any subset of the computer program(s) may employ or invoke machine learning and/or artificial intelligence to perform their respective functions and, accordingly, may participate in federated learning)(Examiner’s Note: “two or more client nodes (102A-102N)” reads on “multiple clients” ; “a central node (104)” reads on “a server”), wherein each client comprises:
a first memory configured to store first instructions (paragraph [0025]…a client storage array (120)); and
a first processor configured to execute the first instructions to (paragraph [0019]… a client node (102A-102N) may include and allocate various resources (e.g., computer processors)):
learn parameters of predetermined multiple operations that are related in that common input data is given and that weighted sum of output data is calculated, and parameters related to calculation of the weighted sum (paragraph [0042]…In Step 202, local data, pertinent to the learning model (received in Step 200), is selected from storage. In one embodiment of the invention, the local data may include a collection of feature-target data tuples. ; paragraph [0043] In Step 204, the learning state of the learning model is adjusted using the local data (or collection of feature-target data tuples) (selected in Step 202). Specifically, in one embodiment of the invention, the collection of feature-target data tuples may first be partitioned into two feature-target data tuple subsets. Thereafter, the learning model may be trained using a first feature-target data tuple subset (i.e., a learning model training set), which may result in the optimization of one or more learning model parameters. A learning model parameter may refer to a model configuration variable that may be adjusted (or optimized) during a training runtime (or epoch) of the learning model. By way of examples, learning model parameters, pertinent to a neural network based learning model (see e.g., FIG. 6), may include, but are not limited to: the weights representative of the connection strengths between pairs of nodes structurally defining the model; and the weight gradients representative of the changes or updates applied to the weights during optimization based on output error of the neural network)(Examiner’s Note: “the learning model may be trained using a first feature-target data tuple subset (i.e., a learning model training set), which may result in the optimization of one or more learning model parameters” reads on “learn parameters of predetermined multiple operations that are related in that common input data is given” ; “the weights representative of the connection strengths between pairs of nodes structurally defining the model; and the weight gradients representative of the changes or updates applied to the weights during optimization based on output error of the neural network” reads on “that weighted sum of output data is calculated, and parameters related to calculation of the weighted sum”); and
send the parameters of the predetermined multiple operations, among the parameters of the predetermined multiple operations and the parameters related to the calculation of the weighted sum, to the server (paragraph [0048]…In Step 210, the compressed local data adjusted learning state (obtained in Step 208) is transmitted to the central node. In one embodiment of the invention, transmission of the compressed local data adjusted learning state may transpire in response to the learning state request (determined to have been received in Step 206). Following the transmission, another learning model may or may not be received from the central node. Should another learning model be received, the new learning model may be configured using/with aggregated learning state, which may encompass non-default values for one or more factors (e.g., weights, weight gradients, and/or weight gradients learning rate) pertinent to the automatic improvement (or “learning”) of the learning model through experience. These non-default values may be derived from the computation of summary statistics (e.g., averaging) on the different compressed local data adjusted learning state, received by the central node, from the various client nodes)(Examiner’s Note: “the compressed local data adjusted learning state (obtained in Step 208) is transmitted to the central node” reads on “send the parameters of the predetermined multiple operations, among the parameters of the predetermined multiple operations and the parameters related to the calculation of the weighted sum”);
wherein the server comprises:
a second memory configured to store second instructions (paragraph [0036]… the central storage array (140)); and
a second processor configured to execute the second instructions to (paragraph [0037]…providing the compressed local data adjusted learning state to the learning state aggregator (146) for processing):
parameter calculation means for recalculating recalculate the parameters of the predetermined multiple operations, based on the parameters of the predetermined multiple operations received from each client (paragraph [0073]…In Step 402, the learning model (configured in Step 400) is distributed to the various client nodes. In Step 404, a trigger for a model update operation is detected. In one embodiment of the invention, the model update operation may reference the task of learning state aggregation as required, in part, by federated learning (described above) (see e.g., FIG. 1A). Further, the trigger may manifest, for example, upon the elapsing of a specified interval of time since the distribution of the learning model (in Step 402). The aforementioned interval of time may allow the various client nodes sufficient time to optimize the learning model using their respective local data through several training iterations (or epochs))(Examiner’s Note: “the model update operation may reference the task of learning state aggregation as required, in part, by federated learning” reads on “parameter calculation means for recalculating recalculate the parameters of the predetermined multiple operations, based on the parameters of the predetermined multiple operations received from each client”); and
server side parameter sending means for sending send the parameters of the predetermined multiple operations to each client (paragraph [0074]…In Step 406, in response to the trigger (detected in Step 404), learning state requests are issued to the various client nodes. Thereafter, in Step 408, compressed local data adjusted learning state is received from each client node. In one embodiment of the invention, the compressed local data adjusted learning state, from a given client node, may refer to learning state that has been optimized based on (or using) the local data, pertinent to the learning model, available on the given client node; and may further refer to learning state that has been compressed through stochastic k-level quantization (described above) (see e.g., FIG. 3))(Examiner’s Note: “server side parameter sending means for sending send the parameters of the predetermined multiple operations to each client” reads on “server side parameter sending means for sending send the parameters of the predetermined multiple operations to each client”).
As to claim 2, Da Silva et al teaches the learning system, wherein the first processor of each client learns the parameters related to the calculation of the weighted sum independently (paragraph [0040]… [0040] FIG. 2 shows a flowchart describing a method for federated learning in infrastructure domains in accordance with one or more embodiments of the invention. The various steps outlined below may be performed by a client node (see e.g., FIGS. 1A and 1B)).
As to claim 3, Da Silva et al teaches the learning system, wherein the number of predetermined multiple operations is lower than the number of clients (paragraph [0018]…the system (100) may include, but is not limited to, two or more client nodes (102A-102N) operatively connected to a central node (104) through a network (106) ; paragraph [0019]… any subset of the computer program(s) may employ or invoke machine learning and/or artificial intelligence to perform their respective functions and, accordingly, may participate in federated learning ; paragraph [0043]…In Step 204, the learning state of the learning model is adjusted using the local data (or collection of feature-target data tuples) (selected in Step 202). Specifically, in one embodiment of the invention, the collection of feature-target data tuples may first be partitioned into two feature-target data tuple subsets)(Examiner’s Note: “the collection of feature-target data tuples may first be partitioned into two feature-target data tuple subsets” reads on “the number of predetermined multiple operations” ; “two or more client nodes (102A-102N)” reads on “is lower than the number of clients”).
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 13 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 4 - 7, 11, and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Da Silva et al (US 2021/0383197) in view of PARK et al (US 2022/0383197) in view of
As to claim 4, Da Silva et al teaches the predetermined multiple operations.
Da Silva et al fails to explicitly show/teach that the predetermined multiple operations are all linear operations.
However, PARK et al teaches predetermined multiple operations are all linear operations (paragraph [0043]…where c represents a scaling factor, v.sub.y represents a selected codeword, and W represents a linear projection matrix. While embeddings need not be shared between client devices or otherwise exposed in this example, model similarity across client devices in the embedding spaces may exist due to the predefined codewords used to train the machine learning model)
Therefore, it would have been obvious for one having ordinary skill in the art, at the time the invention was made, for Da Silva et al to explicitly show/teach that the predetermined multiple operations are all linear operations, as in PARK et al, for the purpose of correctly fitting the data to the model.
As to claim 5, modified Da Silva et al teaches the learning system, wherein the first processor of each client, when the parameters of the predetermined multiple operations and the parameters related to the calculation of the weighted sum are determined, converts the predetermined multiple operations into a single operation based on the parameters of the predetermined multiple operations and the parameters related to the calculation of the weighted sum (paragraph [0042]…In Step 202, local data, pertinent to the learning model (received in Step 200), is selected from storage. In one embodiment of the invention, the local data may include a collection of feature-target data tuples Each feature-target tuple may encompass a feature set (i.e., values pertaining to a set of measurable properties or indicators) and one or more expected (or target) classification and/or prediction values representative of the desired output(s) of the learning model given the feature set. The feature set and expected classification/prediction value(s) may be significant to the objective or application for which the learning model may have been designed and/or configured)(Examiner’s Note: “a collection of feature-target data tuples Each feature-target tuple may encompass a feature set (i.e., values pertaining to a set of measurable properties or indicators) and one or more expected (or target) classification and/or prediction values representative of the desired output(s) of the learning model given the feature set” reads on “converts the predetermined multiple operations into a single operation based on the parameters of the predetermined multiple operations and the parameters related to the calculation of the weighted sum”).
As to claim 6, PARK et al teaches the first processor of each client, when the parameters of the predetermined multiple operations and the parameters related to the calculation of the weighted sum are determined, derives an inference result for given data based on a model determined by the parameters of the predetermined multiple operations and the parameters related to the calculation of the weighted sum (paragraph…[0100] NPUs designed to accelerate training are generally configured to accelerate the optimization of new models, which is a highly compute-intensive operation that involves inputting an existing dataset (often labeled or tagged), iterating over the dataset, and then adjusting model parameters, such as weights and biases, in order to improve model performance. Generally, optimizing based on a wrong prediction involves propagating back through the layers of the model and determining gradients to reduce the prediction error ; paragraph [0101]…NPUs designed to accelerate inference are generally configured to operate on complete models. Such NPUs may thus be configured to input a new piece of data and rapidly process it through an already trained model to generate a model output (e.g., an inference)).
It would have been obvious for the first processor of each client, when the parameters of the predetermined multiple operations and the parameters related to the calculation of the weighted sum are determined, derives an inference result for given data based on a model determined by the parameters of the predetermined multiple operations and the parameters related to the calculation of the weighted sum, for the same reasons as above.
As to claim 7, PARK et al teaches an inference device comprising: a third memory configured to store third instructions: and a third processor configured to execute the third instructions to: derive an inference result for given data based on a model determined by the parameters of the predetermined multiple operations and the parameters related to the calculation of the weighted sum that are obtained by the learning system (paragraph [0099]…An NPU, such as 808, is generally a specialized circuit configured for implementing all the necessary control and arithmetic logic for executing machine learning algorithms, such as algorithms for processing artificial neural networks (ANNs), deep neural networks (DNNs), random forests (RFs), and the like. An NPU may sometimes alternatively be referred to as a neural signal processor (NSP), tensor processing units (TPU), neural network processor (NNP), intelligence processing unit (IPU), vision processing unit (VPU), or graph processing unit.; paragraph…[0100] NPUs designed to accelerate training are generally configured to accelerate the optimization of new models, which is a highly compute-intensive operation that involves inputting an existing dataset (often labeled or tagged), iterating over the dataset, and then adjusting model parameters, such as weights and biases, in order to improve model performance. Generally, optimizing based on a wrong prediction involves propagating back through the layers of the model and determining gradients to reduce the prediction error ; paragraph [0101]…NPUs designed to accelerate inference are generally configured to operate on complete models. Such NPUs may thus be configured to input a new piece of data and rapidly process it through an already trained model to generate a model output (e.g., an inference)).
It would have been obvious for an inference device comprising: a third memory configured to store third instructions: and a third processor configured to execute the third instructions to: derive an inference result for given data based on a model determined by the parameters of the predetermined multiple operations and the parameters related to the calculation of the weighted sum that are obtained by the learning system, 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.
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).
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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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