DETAILED ACTION
This action is in response to the amendments filed on May 4th, 2026. A summary of this action:
Claims 1-5, 7, 9-13, 15, 17-26 have been presented for examination.
Claims 1-2, 9-10, 18-19, 22, 25-26 are objected to because of informalities:
Claims 1-5, 7, 9-13, 15, 17-26 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite
Claims 1-5, 7, 9-13, 15, 17-26 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement
The claims are not rejected under § 102/103, when interpreted in view of MPEP § 2143.03(II): “When evaluating claims for obviousness under 35 U.S.C. 103, all the limitations of the claims must be considered and given weight, including limitations which do not find support in the specification as originally filed (i.e., new matter)...”) in view of the § 112(a) rejections below, as no combination of art of record fairly teaches the particular ordered combination of features recited in the independent claims (including in particular from the “determining an improvement…” to the “emulating…the at least one set…” steps taken in ordered combination as recited). The closest prior art of record is the previously relied upon (Non-final Act. Jan. 2026) Anil, Rohan, et al. "Large scale distributed neural network training through online distillation." arXiv preprint arXiv:1804.03235 (2018), taken in further combination with newly cited Hsu, Tz-Heng, Zhi-Hao Wang, and Aaron Raymond See. "A cloud-edge-smart IoT architecture for speeding up the deployment of neural network models with transfer learning techniques." Electronics 11.14 (2022): 2255. Abstract and § 4 ¶¶ 1-2 taken in further view of one of the following cited references:
Previously cited Zhang, Ran, et al. "Transfer learning with neural networks for bearing fault diagnosis in changing working conditions." Ieee Access 5 (2017): 14347-14357. Abstract § II.C along with § II.D including the steps of the “transferring” procedure
Previously cited Rokni, Seyed Ali, et al. "TransNet: Minimally supervised deep transfer learning for dynamic adaptation of wearable systems." ACM Transactions on Design Automation of Electronic Systems (TODAES) 26.1 (2020): 1-31. Abstract and § 2.1 ¶ 2, as well as § 4 including algorithm 1
Previously cited Shi, Yuan, and Xianze Xu. "Deep federated adaptation: An adaptative residential load forecasting approach with federated learning." Sensors 22.9 (2022): 3264. Abstract and page 4. Also see § 3.1
Chebotar, Yevgen, and Austin Waters. "Distilling knowledge from ensembles of neural networks for speech recognition." Interspeech. 2016. § 3.2
Dean, Jeffrey, et al. "Large scale distributed deep networks." Advances in neural information processing systems 25 (2012). § 4.1
This action is Final
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 .
Response to Arguments/Amendments
Regarding the priority denial
Maintained. Remarks do not challenge the denial expressly. Priority denial was acquiesced to expressly in May 2026 remarks at 25-26: “Applicant therefore does not presently contend that the currently pending amended claims are entitled to the parent's November 8, 2016 filing date. Instead, Applicant prosecutes the presently amended claims on the basis of the present application as filed on October 6, 2022, which is the application that expressly discloses the claimed neural-network species and the claimed parameter-level emulation architecture.”
To clarify, see the table at 27-28 in the remarks, which are admissions by the applicant regarding what presently claimed is not supported in the parent CIP application.
Regarding the § 112 Rejections
Withdrawn in view of amendments. New grounds necessitated by amendment.
With respect to the remarks starting at 33, the Examiner notes that these remarks are generally conclusory and don’t address the concise rationales stated in the rejection. Furthermore, the rejection of dependent claim 6 is unaddressed, and see the § 112(a) rejection below for the amended subject matter of original claim 6 now in the independent claims.
Examiner notes that the cited support in the remarks is not reasonably pertinent to several of the limitations that they are linked to in the table (Remarks, starting at 16), e.g. ¶ 15 states: “This embodiment is illustrated in the accompanying drawings, throughout which like reference letters indicate corresponding parts in the various figures. The embodiments herein will be better understood from the following description with reference to the drawings, in which:” and ¶ 48: “At least one of the plurality of modules/ components of the emulation management controller (180) may be implemented through an AI model. A function associated with the AI model may be performed through memory (120) and the processor (140). The one or a plurality of processors controls the processing of the input data in accordance with a predefined operating rule or the AI model stored in the non-volatile memory and the volatile memory. The predefined operating rule or artificial intelligence model is provided through training or learning” – these cited portions do not support the copying parameter sets as claimed and pointed to in the remarks (rather, see original claims and ¶¶ 46 and 97).
Regarding the § 102/103 Rejection
Withdrawn in view of amendment.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 120 as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
See MPEP 2163(II)(A): "For example, in Hyatt v. Dudas, 492 F.3d 1365, 1371, 83 USPQ2d 1373, 1376-1377 (Fed. Cir. 2007), the examiner made a prima facie case by clearly and specifically explaining why applicant’s specification did not support the particular claimed combination of elements, even though applicant’s specification listed each and every element in the claimed combination. The court found the "examiner was explicit that while each element may be individually described in the specification, the deficiency was lack of adequate description of their combination" and, thus, "[t]he burden was then properly shifted to [inventor] to cite to the examiner where adequate written description could be found or to make an amendment to address the deficiency.""
Also, see MPEP 2163(I) for Lockwood v. Amer. Airlines, Inc., 107 F.3d 1565, 1572, 41 USPQ2d 1961, 1966 (Fed. Cir. 1997).
The disclosure of the prior-filed application, Application No. 15346691, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application.
See the subject matter of the independent claims as expressly recited, as this subject matter has no basis in the 15346691 disclosures.
To clarify, see the recitations of the “actual model” and “reference model” in the present claims, which, when given their BRI in view of the disclosure (e.g. ¶¶ 43, 48, etc.) encompass, but are not limited to, models such as neural networks and other AI models.
See the 15346691, ¶¶ 58-60, wherein the term model is used in an entirely different context, e.g. “The internet model of communication”, and see the remaining portions of the disclosure of 15346691 which do not describe nor even have any contemplations of the subject matter expressly recited in the present claims (e.g. compare the drawings of the two applications). Nor does the ‘691 even mention the use of neural networks or machine learning.
As such, there is insufficient written description support in the ‘691 application as it was filed to support the instant present claims, therefore the instant present claims have the effective filing date of the day on which they, and their corresponding disclosure, were filed.
Claim Objections
Claims 1-2, 9-10, 18-19, 22, 25-26 are objected to because of the following informalities:
Claims 2 and 10 recite “addition” which is not the correct form for the context. Examiner suggests the term “additional”, i.e. “includes additional layers…and additional parameters” – or to change this to a step with verbs of adding, or the use of an article, e.g. “an addition of layers…”
Claim 19 – this recites “AI models” but does not expressly state what “AI” stands for. Examiner suggests using parenthesis, i.e. artificial intelligence (AI) models
The claims have numerous issues with antecedent basis. The Examiner suggests amending the claims such that the first recitation of each distinct element uses articles such as “a”/”an”, later recitations referring back to the same distinct element uses articles such as “the”/”said”, to use disambiguating modifiers (e.g., first, second, etc.) when there are multiple distinct elements with the same base term, and that the use of modifiers for each distinct element is kept consistent. Below is a non-exhaustive list of examples of these issues:
Independent claims (claim 1 as representative): “a plurality of set of parameters” – no article on “set”. Examiner notes later recitations such as “in at least one set of a plurality of set of parameters” indicate that the “set” is intended to be plural, but it is not expressly so, and the later recitations do not refer back to the first
Independent claims (claim 1 as representative): “reward” has no article at first recitation
Independent claims (claim 1 as representative) recites multiple instances of “plurality of parameters”/ “plurality of set[s] of parameter” and the like, and while the context of the claim conveys many of these are associated with a particular model or plurality of models (e.g. “of the reference model”, “with an alternate model of the…actual models”, etc.), the claims do not expressly disambiguate the various parameters themselves. Examiner suggests express disambiguation to ensure clarity, especially for later recitations when referring back to one of these elements, e.g. “a plurality of set[s] of reference parameters …of the reference model”, “at least one set of alternate parameters….associated with an alternate model”, or a similar such express disambiguation (e.g. with the phrase “reference model” as a disambiguating modifier to “parameters”). E.g.:
Independent claims recites (claim 1 as representative): determining, by the electronic device (100), an improvement in at least one set of parameters of a plurality of set of parameters associated with an alternate model of the plurality of actual models based on reward, wherein the reward is determined using a reinforcement learning technique, wherein the alternate model is newly introduced and the plurality of actual models further comprises existing models;… and emulating, by the electronic device (100), the at least one set of parameters associated with the existing models of the plurality of actual models based on the improvement in the at least one set of parameters of the alternate model, wherein each model of the plurality of actual models maintains a unique set of parameters and adjusts one subset of the parameters based on an improvement in one of the plurality of actual models. – Examiner suggests additional disambiguating terms to be used, e.g. “at least one alternate model set of parameters” and “at least one existing model set of parameters” to ensure that when referring back to the element the claim is expressly clear (i.e. current claim recites “the at least one set of parameters associated with the existing models”, but the one set was associated with alternate model expressly in the claim, so the Examiner infers this is a new element of a new set of existing model parameters)
Independent claims (claim 1 as representative): “wherein each model of the plurality of actual models maintains a unique set of parameters and adjusts one subset of the parameters based on an improvement in one of the plurality of actual models.” – at issue is that prior recitations set out a plurality of parameters, and referring back to “the parameters” is not expressly clear which parameters elements. See the above suggestion on the disambiguating modifiers, e.g. “of the unique set of unique parameters” for this limitation, or alternatively “existing model parameters” (Examiner notes prior recitation of “existing models of the actual model”, i.e. not the actual model previously recited) – see ¶ 99: “With the imperfect emulation, each model maintains a unique set of parameters and then adjusts one subset of the parameters based on an improvement in one of the models to the data set”
Independent claims recite multiple “improvement” elements but do not expressly have a disambiguating modifier for each of these to ensure clear claim scope
Claim 18 – the recitation of “are in the plurality of actual models” as a result of “transitions” is ambiguous, for the independent claims already require that the alternate model and existing models are in the/part of the plurality of actual models. Examiner suggests deleting limitation given what is recited in the independent claims already
Claim 22: “reward” was previously recited in the independent and this does not expressly disambiguate from it
Claim 25: lock step was previously recited, but this does not expressly refer back to it
Similar objection for claim 26
Appropriate correction is required.
Claim Rejections - 35 USC § 112(b)
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 5, 13, 22 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. The dependent claims inherit the deficiencies of the claims they depend upon.
MPEP § 2173.05(b)(IV): “A claim term that requires the exercise of subjective judgment without restriction may render the claim indefinite. In re Musgrave, 431 F.2d 882, 893, 167 USPQ 280, 289 (CCPA 1970). Claim scope cannot depend solely on the unrestrained, subjective opinion of a particular individual purported to be practicing the invention. Datamize LLC v. Plumtree Software, Inc., 417 F.3d 1342, 1350, 75 USPQ2d 1801, 1807 (Fed. Cir. 2005));”
Representative 5 recites the phrase “substantial improvement”, wherein the term “substantial” is a subjective term that renders the claim indefinite because there is no standard provided in the instant disclosure (¶ 99) for POSITA to ascertain the scope of the present claims without relying on their own unrestrained, subjective opinion when practicing the invention. Claim 13 rejected under similar rationale.
Claim 22: “wherein performing the transfer learning comprises progressively improving to a new ensemble,” – see ¶ 103. This is a subjective phrase in this context, and the specification at ¶ 103 does not provide an objective standard to ascertain the definitive scope.
Claim 22 further recites “closer copy” but “closer” is also a subjective term, and no standard is provided (¶ 103)
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-5, 7, 9-13, 15, 17-26 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The dependent claims inherit the deficiencies of the claims they depend upon.
See MPEP 2163(II)(A): "For example, in Hyatt v. Dudas, 492 F.3d 1365, 1371, 83 USPQ2d 1373, 1376-1377 (Fed. Cir. 2007), the examiner made a prima facie case by clearly and specifically explaining why applicant’s specification did not support the particular claimed combination of elements, even though applicant’s specification listed each and every element in the claimed combination. The court found the "examiner was explicit that while each element may be individually described in the specification, the deficiency was lack of adequate description of their combination" and, thus, "[t]he burden was then properly shifted to [inventor] to cite to the examiner where adequate written description could be found or to make an amendment to address the deficiency.""
Also, see MPEP 2163(I) for Lockwood v. Amer. Airlines, Inc., 107 F.3d 1565, 1572, 41 USPQ2d 1961, 1966 (Fed. Cir. 1997).
Representative claim 1 recites:
modifying, by the electronic device (100), the current state of the actual model to emulate the target state to be achieved by the actual model based on the at least one state of the reference model by copying a plurality of set of parameters of a neural network of the reference model, wherein modifying the current state minimizes an error between the current state of the actual model and the target state to be achieved by the actual model;
This particular combination is not sufficiently described – see ¶¶ 97, 12, 46 for the copying of the parameters as the modification.
At issue is that this particular combination of said particular step of modifying in combination with the minimizes an error is not sufficiently described – see ¶¶ 9, 42 for the minimizing of error, but there is no linkage between that modification and when the modification is that of copying the parameters.
Representative claim 1 recites:
determining, by the electronic device (100), an improvement in at least one set of parameters of a plurality of set of parameters associated with an alternate model of the plurality of actual models based on reward, wherein the reward is determined using a reinforcement learning technique, wherein the alternate model is newly introduced and the plurality of actual models further comprises existing models;
This is not sufficiently described.
See ¶ 44 and the originally filed claim 6. See the § 112(b) rejection in the Jan non-final act. at 10-12. Also see ¶ 10. Applicant’s remarks (May 2026, at 17) submitted ¶¶ 13, 46, 102 102, 105-106, however these do not mention the alternate model at issue.
To clarify, these claimed recitations as presented remedies the § 112(b) issue by introducing a § 112(a) issue, for, as was stated in the prior § 112(b) rejection, the specification shed no clarifying light on a non-speculative interpretation of the claim. The specification as well as the original claim 6, and its parallel, is indefinite as to the subject matter recited herein this limitation, specifically what the relationship between the “alternate model” is with the actual model and the plurality of actual models and the existing models.
To clarify, the original claims and the specification provide multiple distinct speculative scopes in what the “alternate model” is in relation to the other models, and this claim, as now presented, has picked one particular way to relate it to the other models, but the specification does not sufficiently describe that this is the particular way it is related to the other models, and rather, as was noted in the prior § 112(b) rejection, there are multiple distinct scopes as to what ¶ 44 and original claim 6 convey, and it would require considerable speculation to arrive at any one of them, with no clear context provided in the disclosure to guide POSITA to any one of the scopes, let alone the particular combination now claimed (see the other models in the claim, and how they now related to each other, contrast with specification and original claims for more clarity, in particular note the relation of the alternate model as presently recited when compared to original claim 6 and ¶ 44).
To further clarify on this, see ¶ 42: “The modification management engine (186) is configured to determine a target state to be achieved by the at least one actual model (182a) based on the at least one state of the reference model (184), determine a deviation of the current state of the at least one actual model with respect to the target state to be achieved by the actual models (182a-N); and modify the current state of the at least one actual models (182a) to emulate the target state to be achieved by the at least one actual model (182a) based on the at least one state of the reference model (184). Modifying the current state of the at least one actual model to emulate the target state to be achieved by the at least one actual model (182a) minimizes an error between the current state of the at least one actual model (182a) and the target state to be achieved by the at least one actual model (182a).”- and in ¶ 44: “The modification management engine (186) is also configured to determine an improvement in at least one set of parameters of a plurality of set of parameters associated with the actual model (182a) of a plurality of actual models (182aN) based on reward. The reward is determined using a reinforcement learning technique, where the actual model (182a) is an alternate model newly introduced and the plurality of models are existing models; and…” – i.e. it is the same part number # 182a in both of these paragraphs.
As such, the specification does not provide sufficient written description support for the particular combination of features now claimed.
Independent claims (using 1 as representative) now recite: and emulating, by the electronic device (100), the at least one set of parameters associated with the existing models of the plurality of actual models based on the improvement in the at least one set of parameters of the alternate model, wherein each model of the plurality of actual models maintains a unique set of parameters and adjusts one subset of the parameters based on an improvement in one of the plurality of actual models.
See original claim 6, which does not recite the bolded feature. See ¶ 44 of the specification as discussed above. Note that this claim further requires that “each model of the plurality”, i.e. including the alternate model now recited to maintain a unique set of parameters and adjusts one subset based on an improvement in one of the plurality of actual models. See ¶¶ 99-100, discussing fig. 9 and 10 respectively. In particular, note the lack of any combination of the features of ¶ 99 and ¶ 100 in the specification, let alone in this particular manner now claimed. E.g. note in ¶ 100: “When there is improvement with the Alt Model, then the existing
models can replicate the newer Alt Model. Or the Alt Model can transition in a more
gradual manner where there are both models in the ensemble” – but there is no discussion of what happens to the alt model when there is an improvement in a different model of the actual models as within the scope of this claim, in comparison in ¶ 99: “With the imperfect emulation, each model maintains a unique set of parameters and then adjusts one subset of the parameters based on an improvement in one of the models to the data set” – which is not what ¶ 100 is describing, nor does ¶ 99 even mention a new alternate model in its embodiment.
In other words, what is presently claimed is an undescribed amalgamation of multiple different features in the written description, wherein the written description does not sufficiently describe this particular claimed combination in the written possession of the application at the effective time of filing.
Claim 18: The method as claimed in claim 3, wherein the alternate model transitions such that both the alternate model and the existing models are in the plurality of actual models.
Not sufficiently described – see ¶ 100: “Referring to the FIG. 10, a scenario where a new model (referred to as Alt model in the FIG. 10) is introduced into the parallel ensembles… When there is improvement with the Alt Model, then the existing models can replicate the newer Alt Model. Or the Alt Model can transition in a more gradual manner where there are both models [the existing models and the alt model, based on the structure of this sentence] in the ensemble” - this does not sufficient describe what is particularly claimed.
Claim 19: The method as claimed in claim 1, further comprising: providing, by a first client, a first model to a clearing house for AI models; and providing, by the clearing house, a replica of the first model to a second client for transfer learning. as well as claim 20: The method as claimed in claim 19, further comprising returning, by the second client, an enhanced copy of the first model to the clearing house, wherein the enhanced copy includes an enhancement added as a new recognizer or a new task in a Multi-Task system.
See fig. 11-12, as described in ¶¶ 101 to 105 – at issue is there is no clear linkage between this portion of the disclosure, and the particular combination recited in the independent claims, i.e. it’s a distinctly different embodiment from what is found in the independent claims, and the specification does not provide sufficient written description support for this particular combination.
Claim 21: The method as claimed in claim 18, further comprising performing transfer learning from one ensemble to another ensemble. And claim 22: The method as claimed in claim 21, wherein performing the transfer learning comprises progressively improving to a new ensemble, and wherein the electronic device maintains a first model that is a closer copy to an original model and is configured to process new data and a second model that has more layers or fewer layers than the first model and is configured to train using reinforcement learning to improve a reward. – see ¶ 103 – at issue again is the particular combination of these features in ¶ 103 with the other features recited in these claims (see the independent claims), as there is no clear linkage in the original disclosure, let alone one that supports this particular combination. Furthermore, ¶ 103 does not sufficiently describe the “fewer layers” in this claim.
Claim 24:
The method as claimed in claim 23, wherein replaying the data sequences comprises executing a catch-up protocol using a distributed event-based log having monotonically increasing offsets per topic and a second topic distinct from the distributed event-based log, the catch-up protocol comprising:
receiving an indication of an intent to catch up from a consumer;
transmitting a catch-up message including a first offset identifying a message in the distributed event-based log;
consuming, by the consumer, messages from the distributed event-based log at a rate faster than a rate at which a producer writes messages to the distributed event- based log until the first offset is reached;
transmitting, by the consumer, an almost-caught-up message including the first offset;
transmitting a pause message including a second offset;
consuming, by the consumer, messages until the second offset is reached;
and transmitting, by the consumer, a caught-up message, after which the producer and the consumer proceed in lock step.
See claim 23 and see ¶ 100 (Examiner noting claim 23 is inheriting the deficiencies from the claims it depends upon) then see ¶¶ 82-85 – the instant disclosure does not sufficiently describe this particular combination of features, for it does not describe such a particular linkage between separately disclosed elements.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Mnih, Volodymyr, et al. "Asynchronous methods for deep reinforcement learning." International conference on machine learning. PmLR, 2016. Abstract and § 2 ¶ 1, then see § 4 ¶ 2
Nair, Arun, et al. "Massively parallel methods for deep reinforcement learning." arXiv preprint arXiv:1507.04296 (2015). Abstract and § 3.1
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID A. HOPKINS whose telephone number is (571)272-0537. The examiner can normally be reached Monday to Friday, 10AM to 7 PM EST.
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, Ryan Pitaro can be reached at (571) 272-4071. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/David A Hopkins/Primary Examiner, Art Unit 2188
Prosecution Insights