Notice of Pre-AIA or AIA Status
The present application 18/246,205, filed on 3/22/2023 (or after March 16, 2013), is being examined under the first inventor to file provisions of the AIA (First Inventor to File).
In the event the determination of the status of the application as subject to AIA 35
U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This application is a 371 of PCT/JP2021/030191 filed on 08/18/2021
DETAILED ACTION
Response to RCE-1
Claims 1-3,5-9,11-13,15-16 are pending in this application.
Examiner acknowledges applicant’s amendment filed on 6/1/2026
A request for continued examination under 37 CFR 1.114, including the fee set
forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this
application is eligible for continued examination under 37 CFR 1.114, and the fee set
forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action
has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on
6/1/2026 has been entered
Drawings
The Drawings filed on 3/22/2023 are acceptable for examination purpose.
Priority
Acknowledgment is made of applicant’s claim for JAPAN foreign priority under
35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. JAPAN
application # 2020-172583 filed on 10/13/2020.
Response to Arguments
Applicant's arguments filed 6/1/2026 with respect to claims 1-3,5-9,11-13,15-16 have been fully considered but they are not persuasive, for examiner’s response, see discussion below:
35 U.S.C. 112(b)
In view of amendment to claims 1,5-7,11-12,15, the rejection under 35 USC 112(b) as set forth in the previous office action is hereby withdrawn.
35 USC § 101
In view of applicant’s amendment, remarks, the rejection under 35 USC § 101 as set forth in the previous office action is hereby withdrawn.
At page 20-21, claim 1, applicant argues:
Sharma does not teach selecting, among a plurality of training methods, an optimum training method for the training of the models, where the selected optimum training method is within a hardware specification associated with an edge device of the plurality of edge devices. Further, Sharma does not teach that the hardware specification associated with the edge device includes at least one of a memory capacity, an operation performance, an operation time, or a power associated with the edge device, the additional cited reference Zhou does not remedy the deficiencies of Sharma,………Even if the reference are combined as suggested ………the combination still does not teach or suggest selecting a training method………..implementable within the physical hardware sources limits (e.g. memory, power, etc) of the edge device performing the training
Examiner’s response:
As to the above argument, as best understood by the examiner, the prior art of Sharma is directed to training data sets in the plurality of training models particularly defining training acquisition parameters and accordingly matching the models in the training the selected model (Sharma: Abstract). Sharma teaches training acquisition parameters from various sources and plurality of specification information corresponds to different types of model data, each of which proposing training acquisition parameters), it is further noted that the prior art of Sharma teaches hardware processors, hardware, software module, fig 13 corresponds to hardware specification in building computer system.
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It should be noted that Sharma teaches training model on the match[ing] datasets using plurality of edge devices (Sharma: 0056), including both hardware and software module (fig 13)
It is however, noted that Sharma does not teach “select an optimum training method”, “optimum training method within a range of a specification associated with the edge device”, “a memory capacity associated with the edge device, , an operation time associated with the edge device, or a power associated with the edge device, although Sharma teaches selecting, train model, model aggregator and repository (fig 1, fig 4) and model optimizer fig 7, element 726.
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On the other hand, KASARAGOD disclosed “select an optimum training method”, “optimum training method within a range of a specification associated with the edge device” (Kasaragod: 0028-0029, 0032,0045, fig 1 – Kasaragod teaches edge devices include computational model, machine learning models not only data collection, but efficiently training on the training data to predict and perform operations in a distributed manner, while redundant data being discarded during the process, thereby improving the confidence level for the prediction, then result manager determines the correct results).
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Kasaragod disclosed “a memory capacity associated with the edge device” (Kasaragod: fig 7, fig 8B, 0107-0109, 0112 – Kasaragod teaches size of one or more memories associated with model training service used by the edge device(s), further edge device(s) or model trainer receive data from the data collector and edge device generates an update to the local model on the analyses of the received data)
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Kasaragod disclosed an operation time associated with the edge device, or a power associated with the edge device” (Kasaragod: fig 1, 0050,0075,0147-0148 – Kasaragod teaches time-series data collection ad performs and generate the data, while edge device(s) may determine the amount of time and/or energy required for model to process the data with respect to defined threshold amount).
It would have been obvious to a person of ordinary skill in the art at the time of filing the claimed invention training models for IoT devices particularly analyze global data from other edge devices on the networks of Kasaragod et al., into matching models with the training datasets based on evaluating the training parameters of Sharma et al., because both Sharma, Kasaragod teaches multiple machine learning models configured to generate training data sets (Sharma: Abstract, fig 2, fig 5; Kasaragod: Abstract, fig 1-2) and they both are from the same field of endeavor. Because both Sharma, Zhou teaches multiple machine learning models in generating training data sets it would have been obvious to one skill ed in the art to substitute and/or modify one method for the other particularly, training data sets on both local and remote network in prediction of the accurate, while updates to the respective edge devices of new versions both local and remote models (Kasaragod: 0028-0029), thereby prediction (element 120) to update and/or modify the local model (element 108) to improve the accuracy of the model(s),(Kasaragod: 0046) thus improves overall quality and reliability of the system.
Examiner applies above arguments to claims 5-6,7, 11,12
b) At page 21, examiner noted applicant’s remarks on dependent claims 2,3,8,9,15, however, examiner applies above argument to dependent claims 2,3,8,9,15
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3,5-9,11-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sharma et al., (hereafter Sharma), US Pub. No. 2021/0042645 based on provisional application filed Aug, 2019 in view of Kasaragod et al., (hereafter Kasaragod) US Pub. No. 2019/0036716 published Jan, 2019
As to claim 1,5-6 (Currently Amended) Sharma teaches a system which including “An information processing apparatus, comprising:” (Sharma: fig 1)
a memory configured to store a correspondence relationship (Sharma: fig 13,0211) between a training method for a first model and task information of the model; (Sharma: fig 1) and
circuitry configured to: (Sharma: fig 13,0211-0212)
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select an training method for the task information, wherein the task information is input from an edge device (Sharma: Abstract, fig 1, 0055-0056 – Sharma teaches training models implementing on training data sets that including training datasets using plurality of edge devices); and
output the training method to the edge device, (Sharma: 0056, 0064) wherein the memory is further configured to store a plurality of pieces of hardware specification information associated with a plurality of training methods” (Sharma: fig 1-2,0075 – Sharma teaches training acquisition parameters from various sources and plurality of specification information corresponds to different types of model data, each of which proposing training acquisition parameters), it is further noted that the prior art of Sharma teaches hardware processors, hardware, software module, fig 13 corresponds to hardware specification in building computer system.
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“the plurality of pieces of hardware specification information is for implementation of the plurality of training methods” (Sharma: fig 2, fig 3-4, 0106-0111 – Sharma teaches overall workflow models defining cloud learning modules where multiple machine learning models associated with training data sets and plurality databases) ,
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“the plurality of pieces of hardware specification information is stored in association with the plurality of training methods” (Sharma: fig 3-4, 0111, - Sharma teaches training data sets with respect to multiple model owners and model aggregator stored in the database element 318 and 418 of workflow specification information respectively,
“the plurality of training methods includes the training method” (Sharma: 0123-0124 – Sharma teaches multiple data models operating respective training data and training models stored in model repository element 536),
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the circuitry is further configured to select, among the plurality of training methods, the training method within a specification associated with the edge device” (Sharma: 0053, 0056-0057,0064), and
“in the edge device, the hardware specification is available for training of the first model” (Sharma: 0056-0057,0060 – Sharma teaches training datasets of respective machine learning models stored plurality of edge devices accompanied with model metadata).
“the hardware specification associated with the edge device (Sharma : 0056,0064,0137, fig 13 – Sharma teaches edge device supporting both hardware and software specification), “the hardware specification is available for training of the first model“(Sharma: Abstract, fig 1, fig 5 – Sharma teaches training models with training datasets supporting edge devices both pre processing and post training environment)
“an operation performance associated with the edge device” (Sharma: 0071-0072, 0122 – Sharma teaches not only ensuring data security, privacy of the training data model(s), but also performance of the model based on combining all the data together in one location, further the training model is put to test for performance against any validation set of data as detailed in 0122); “and in the edge device, the hardware specification is available for training of the first model” (Sharma: fig 13, 0061)
It is however, noted that Sharma does not teach “select an optimum training method”, “optimum training method within a range of a specification associated with the edge device”, “a memory capacity associated with the edge device, , an operation time associated with the edge device, or a power associated with the edge device, although Sharma teaches selecting, train model, model aggregator and repository (fig 1, fig 4) and model optimizer fig 7, element 726.
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On the other hand, KASARAGOD disclosed “select an optimum training method”, “optimum training method within a range of a specification associated with the edge device” (Kasaragod: 0028-0029, 0032,0045, fig 1 – Kasaragod teaches edge devices include computational model, machine learning models not only data collection, but efficiently training on the training data to predict and perform operations in a distributed manner, while redundant data being discarded during the process, thereby improving the confidence level for the prediction, then result manager determines the correct results).
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Kasaragod disclosed “a memory capacity associated with the edge device” (Kasaragod: fig 7, fig 8B, 0107-0109, 0112 – Kasaragod teaches size of one or more memories associated with model training service used by the edge device(s), further edge device(s) or model trainer receive data from the data collector and edge device generates an update to the local model on the analyses of the received data)
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Kasaragod disclosed an operation time associated with the edge device, or a power associated with the edge device” (Kasaragod: fig 1, 0050,0075,0147-0148 – Kasaragod teaches time-series data collection ad performs and generate the data, while edge device(s) may determine the amount of time and/or energy required for model to process the data with respect to defined threshold amount).
It would have been obvious to a person of ordinary skill in the art at the time of filing the claimed invention training models for IoT devices particularly analyze global data from other edge devices on the networks of Kasaragod et al., into matching models with the training datasets based on evaluating the training parameters of Sharma et al., because both Sharma, Kasaragod teaches multiple machine learning models configured to generate training data sets (Sharma: Abstract, fig 2, fig 5; Kasaragod: Abstract, fig 1-2) and they both are from the same field of endeavor. Because both Sharma, Zhou teaches multiple machine learning models in generating training data sets it would have been obvious to one skill ed in the art to substitute and/or modify one method for the other particularly, training data sets on both local and remote network in prediction of the accurate, while updates to the respective edge devices of new versions both local and remote models (Kasaragod: 0028-0029), thereby prediction (element 120) to update and/or modify the local model (element 108) to improve the accuracy of the model(s),(Kasaragod: 0046) thus improves overall quality and reliability of the system.
As to claim 2, the combination of Sharma, Kasaragod disclosed wherein the circuitry is further configured to select the optimum training method for the task information, based on a similarity of a feature vector representing the task information” (Kasaragod: Kasaragod: (Kasaragod: 0133-0134,0138)
As to claim 3, 9, the combination of Sharma, Kasaragod disclosed “training data set of a relevant model by using meta- learning “ (Sharma: 0053, fig 10,1002). The prior art of Kasaragod disclosed “wherein the feature vector is calculated from a training data set of a relevant model by using meta- learning” (Kasaragod: 0050-0051, 0075-0076, 0133-0134,0138 – Zhou teaches using vector machine classifier technique in training machine learning models in various categories including calculating, comparison of respective training data sets from relevant machine learning models) .
Claim 4. (Canceled)
As to Claim 7,11,12. (Currently Amended), Sharma teaches a system which including “an information processing apparatus, comprising: (Sharma: fig 1)
circuitry configured to: (Sharma: fig 13,0211-0212)
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“collect a first data set for training of a model” (Sharma: fig 5 – Sharma teaches model repository element 536 stores multiple machine learning models);
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“extract task information of the model based on the collected first data set” (Sharma: 0058, Abstract, fig 1-2, Sharma teaches training models implementing on training data sets from multiple model sources acquiring respective task parameters);
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“calculate a hardware specification associated with the information processing apparatus for the training of the model” (Sharma: fig 5-6, 0124, 0129-0134 – Sharma teaches security setting, log records and respective metrics maintained as statistical information particularly with respect to training data sets to train the selected model on training data in the database as shown in fig 5, further Sharma teaches aggregator configuring the not only data path for training model, but also used in calculating aggregation in improving training model as detailed in fig 6)
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“wherein the hardware specification associated with the information processing apparatus includes at least” (Sharma: 0056,0064,0137, fig 13 – Sharma teaches edge device supporting both hardware and software specification)
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“an operation performance associated with the information processing apparatus” (Sharma: 0071-0072, 0122 – Sharma teaches not only ensuring data security, privacy of the training data model(s), but also performance of the model based on combining all the data together in one location, further the training model is put to test for performance against any validation set of data as detailed in 0122)
“acquire an training method for the task information from an external apparatus, (0058, Abstract, fig 1-2, Sharma teaches training models implementing on training data sets from multiple model sources acquiring respective task parameters) wherein the acquired training method is implementable within a range of the calculated specification” (Sharma: fig 5-6, 0124, 0129-0134 – Sharma teaches security setting, log records and respective metrics maintained as statistical information particularly with respect to training data sets to train the selected model on training data in the database as shown in fig 5, further Sharma teaches aggregator configuring the not only data path for training model, but also used in calculating aggregation in improving training model as detailed in fig 6and
“train the model by using the acquired training method” (Sharma: fig 6-7 – Sharma teaches multiple training models acquiring respective training data data set in model building in training models that including model optimizer fig 7, element 726)
It is however, noted that Sharma does not teach “a memory capacity associated with the information processing apparatus”, an operation time associated with the information processing apparatus, or a power associated with the information processing apparatus”, although Sharma teaches selecting, train model, model aggregator and repository (fig 1, fig 4) and model optimizer fig 7, element 726.
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Kasaragod disclosed “a memory capacity associated with the information processing apparatus” (Kasaragod: fig 7, fig 8B, 0107-0109, 0112 – Kasaragod teaches size of one or more memories associated with model training service used by the edge device(s), further edge device(s) or model trainer receive data from the data collector and edge device generates an update to the local model on the analyses of the received data)
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Kasaragod disclosed “an operation time associated with the information processing apparatus, or a power associated with the information processing apparatus” (Kasaragod: fig 1, 0050,0075,0147-0148 – Kasaragod teaches time-series data collection performs and generate the data, while edge device(s) may determine the amount of time and/or energy required for model to process the data with respect to defined threshold amount).
It would have been obvious to a person of ordinary skill in the art at the time of filing the claimed invention training models for IoT devices particularly analyze global data from other edge devices on the networks of Kasaragod et al., into matching models with the training datasets based on evaluating the training parameters of Sharma et al., because both Sharma, Kasaragod teaches multiple machine learning models configured to generate training data sets (Sharma: Abstract, fig 2, fig 5; Kasaragod: Abstract, fig 1-2) and they both are from the same field of endeavor. Because both Sharma, Zhou teaches multiple machine learning models in generating training data sets it would have been obvious to one skill ed in the art to substitute and/or modify one method for the other particularly, training data sets on both local and remote network in prediction of the accurate, while updates to the respective edge devices of new versions both local and remote models (Kasaragod: 0028-0029), thereby prediction (element 120) to update and/or modify the local model (element 108) to improve the accuracy of the model(s),(Kasaragod: 0046) thus improves overall quality and reliability of the system.
As to claim 8, the combination of Sharma, Zhou disclosed “wherein the circuitry is further configured to perform inference by using the trained model” (Sharma: fig 2, fig 5)
Claim 10. (Canceled)
Claim 13. (Currently Amended) Sharma teaches a system which including “A learning system, comprising” (Sharma: fig 2-3, 0053,0062,0064)
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“a first apparatus including first circuitry configured to collect a data set and train a model” (Sharma: fig 2 – Sharma teaches training acquisition data from multiple sources); and
a second apparatus including second circuitry configured to output a training method for the model to the first apparatus, (Sharma: fig 2 – Sharma teaches training acquisition data from multiple sources) wherein
the first circuitry is further configured to extract task information of the model based on the collected data set, (Sharma: Abstract, fig 1, fig 5, 0055-0056,0122-0123 – Sharma teaches training models implementing on training data sets that including training datasets) and
“the second circuitry is further configured to” (Sharma: fig 2 Sharma teaches training acquisition data from multiple sources):
“select an training method for the task information of the first apparatus by using a database, wherein the database stores” (Sharma: 0123-0124 – Sharma teaches multiple data models operating respective training data and training models stored in model repository element 536)
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“a correspondence relationship between the training method of the for a model and the task information of the model” (Sharma: fig 5 – Sharma teaches training datasets stored in database element 552 accessed by model element 514 to train model element 524 sent to the model repository where multiple models able to select respective task model), and
“a plurality of pieces of hardware specification information associated with a plurality of training methods” (Sharma: fig 1-2,0075 – Sharma teaches training acquisition parameters from various sources and plurality of specification information corresponds to different types of model data, each of which proposing training acquisition parameters)
“the plurality of pieces of hardware specification information is for implementation of the plurality of training methods” (Sharma: fig 1-2,0075 – Sharma teaches training acquisition parameters from various sources and plurality of specification information corresponds to different types of model data, each of which proposing training acquisition parameters)
“the plurality of pieces of hardware specification information is stored in association with the plurality of training methods respectively,” (fig 1-2, fig 5 – Sharma teaches multiple training datasets in relationship with multiple model),
“the plurality of training methods includes the training method, the training method is selected among the plurality of training methods” (Sharma: fig 1-2,0075 – Sharma teaches training acquisition parameters from various sources and plurality of specification information corresponds to different types of model data, each of which proposing training acquisition parameters),
“in the first apparatus, the hardware specification is available for the training of the model” (Sharma: 0056-0057,0060 – Sharma teaches training datasets of respective machine learning models stored plurality of edge devices accompanied with model metadata).: and
“output the training method to the first apparatus” (Sharma: fig 6-7 – Sharma teaches model improved version with respect to validating (fig 6) and outputting respective training method associated with model as detailed in fig 7)
It is however, noted that Sharma does not teach “select an optimum training method”, “optimum training method within a range of a specification associated with the first apparatus”, “a memory capacity associated with the first apparatus, , an operation time associated with the edge device, or a power associated with the first apparatus, although Sharma teaches selecting, train model, model aggregator and repository (fig 1, fig 4) and model optimizer fig 7, element 726.
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On the other hand, KASARAGOD disclosed “select an optimum training method”, “optimum training method within a range of a specification associated with the first apparatus” (Kasaragod: 0028-0029, 0032,0045, fig 1 – Kasaragod teaches edge devices include computational model, machine learning models not only data collection, but efficiently training on the training data to predict and perform operations in a distributed manner, while redundant data being discarded during the process, thereby improving the confidence level for the prediction, then result manager determines the correct results).
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Kasaragod disclosed “a memory capacity associated with the first apparatus” (Kasaragod: fig 7, fig 8B, 0107-0109, 0112 – Kasaragod teaches size of one or more memories associated with model training service used by the edge device(s), further edge device(s) or model trainer receive data from the data collector and edge device generates an update to the local model on the analyses of the received data)
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Kasaragod disclosed “an operation time associated with the edge device, or a power associated with the first apparatus” (Kasaragod: fig 1, 0050,0075,0147-0148 – Kasaragod teaches time-series data collection performs and generate the data, while edge device(s) may determine the amount of time and/or energy required for model to process the data with respect to defined threshold amount).
It would have been obvious to a person of ordinary skill in the art at the time of filing the claimed invention training models for IoT devices particularly analyze global data from other edge devices on the networks of Kasaragod et al., into matching models with the training datasets based on evaluating the training parameters of Sharma et al., because both Sharma, Kasaragod teaches multiple machine learning models configured to generate training data sets (Sharma: Abstract, fig 2, fig 5; Kasaragod: Abstract, fig 1-2) and they both are from the same field of endeavor. Because both Sharma, Zhou teaches multiple machine learning models in generating training data sets it would have been obvious to one skill ed in the art to substitute and/or modify one method for the other particularly, training data sets on both local and remote network in prediction of the accurate, while updates to the respective edge devices of new versions both local and remote models (Kasaragod: 0028-0029), thereby prediction (element 120) to update and/or modify the local model (element 108) to improve the accuracy of the model(s),(Kasaragod: 0046) thus improves overall quality and reliability of the system.
Claim 14. (Canceled)
As to claim 15, the combination of Sharma, Kasaragod disclosed
the memory capacity for implementation of a corresponding training method of the plurality of training methods (Kasaragod: fig 7, fig 8B, 0107-0109, 0112),
“the operation performance for the implementation of the corresponding training method of the plurality of training methods” (Sharma: 0072, 0122, fig 2-5)
“the operation time for the implementation of the corresponding training method of the plurality of training methods” (Sharma: fig 1-2,0075), or
The power for the implementation of the corresponding training method of the plurality of training methods” (Kasaragod: fig 1, 0050,0075,0147-0148)
As to claim 16, the combination of Sharma, Kasaragod disclosed:
“an initial model parameter associated with the first model” (Sharma: Abstract, fig 1, fig 5),
“a hyperparameter associated with the first model” (Sharma: fig 10, 0156-0157), or
“a second model that is usable in the training of the first model” (Sharma: fig 5, 0123-0125).
Conclusion
The prior art made of record
a. US Pub. No. 2021/0042645
b. US Pub. No. 2019/0036716
Examiner's Note: Examiner has cited particular columns and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
SEE MPEP 2141.02 [R-5] VI. PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, INCLUDING DISCLOSURES THAT TEACH AWAY FROM THE CLAIMS: A prior art reference must be considered in its entirety, i.e., as a whole, including portions that would lead away from the claimed invention. W.L. Gore & Associates, Inc. v. Garlock, Inc., 721 F.2d 1540, 220 USPQ 303 (Fed. Cir. 1983), cert. denied, 469 U.S. 851 (1984) In re Fulton, 391 F.3d 1195, 1201,73 USPQ2d 1141, 1146 (Fed. Cir. 2004). >See also MPEP §2123.
In the case of amending the Claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
The prior art made of record, listed on form PTO-892, and not relied upon, if any, is considered pertinent to applicant's disclosure
Authorization for Internet Communications
The examiner encourages Applicant to submit an authorization to communicate with the examiner via the Internet by making the following statement (from MPEP 502.03):
“Recognizing that Internet communications are not secure, I hereby authorize the USPTO to communicate with the undersigned and practitioners in accordance with 37 CFR 1.33 and 37 CFR 1.34 concerning any subject matter of this application by video conferencing, instant messaging, or electronic mail. I understand that a copy of these communications will be made of record in the application file.”
Please note that the above statement can only be submitted via Central Fax (not Examiner's Fax), Regular postal mail, or EFS Web using PTO/SB/439.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Srirama Channavajjala whose telephone number is 571-272-4108. The examiner can normally be reached on Monday-Friday from 8:00 AM to 5:30 PM Eastern Time.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Gorney, Boris, can be reached on (571) 270- 5626. The fax phone numbers for the organization where the application or proceeding is assigned is 571-273-8300 Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free)
/Srirama Channavajjala/Primary Examiner, Art Unit 2154
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