DETAILED ACTION
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 .
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 05/14/2026 has been entered.
Response to Amendment
Claims 1-14 remain pending within the application.
The amendments filed 05/14/2026 are sufficient to overcome the 112 rejections previously set forth in the Non-Final Office Action mailed 02/18/2026. The rejections have been withdrawn.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
"an acquisition unit that acquires information indicating a dropout rate in training of a model" in claim 13. This element is interpreted under 35 U.S.C. 112(f) as a processor (Fig. 3 and ¶[0101] “The control unit 40 is implemented by, for example, a central processing unit (CPU), a micro processing unit (MPU), or the like executing various programs… the control unit 40 includes an acquisition unit 41, a determination unit 42, a reception unit 43, a generation unit 44, and a provision unit 45.”), with the algorithm described in the specification (¶[0102-0103] “The acquisition unit 41 acquires the learning data used for the training of the model. For example, when once various pieces of data to be used as the learning data and labels assigned to the various pieces of data are received from the terminal apparatus 3, the acquisition unit 41 registers the received data and labels in the learning data database 31 as the learning data... The acquisition unit 41 acquires information indicating the dropout rate.”).
“a determination unit that determines a unit size of a hidden layer…” in claim 13. This element is interpreted under 35 U.S.C. 112(f) as a processor (Fig. 3 and ¶[0101] “The control unit 40 is implemented by, for example, a central processing unit (CPU), a micro processing unit (MPU), or the like executing various programs… the control unit 40 includes an acquisition unit 41, a determination unit 42, a reception unit 43, a generation unit 44, and a provision unit 45.”), with the algorithm described in the specification (¶[0079] and ¶[0097] “determine the unit size of the embedding layer of the first-type partial model by using a function indicating a relationship between the dropout rate and the unit size of the embedding layer”).
“a generation unit that generates the model having a size based on the dropout rate…” in claim 13. This element is interpreted under 35 U.S.C. 112(f) as a processor (Fig. 3 and ¶[0101] “The control unit 40 is implemented by, for example, a central processing unit (CPU), a micro processing unit (MPU), or the like executing various programs… the control unit 40 includes an acquisition unit 41, a determination unit 42, a reception unit 43, a generation unit 44, and a provision unit 45.”), with the algorithm described in the specification (¶[0110] “The generation unit 44 generates the model by performing batch normalization after dropout based on the dropout rate.”).
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites "training the model". It is unclear whether “training the model” refers to the “first partial model”, the “second partial model”, or the model comprising both, as recited in claim 1. There is insufficient antecedent basis for these limitations. Claims 13 and 14 are substantially similar to claim 1 and thus are rejected on the same basis as claim 1. Dependent claims 2-12 inherit the deficiency and therefore are rejected on the same basis.
Claims 2-12 recite “the model". It is unclear whether “the model” in claims 2-12 refers to the “first partial model”, the “second partial model”, or the model comprising both, as recited in claim 1. There is insufficient antecedent basis for this limitation in claims 2-12. For examination purposes, the examiner is interpreting “the model” to refer to the model comprising both the first partial model and the second partial model.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-14 are rejected under 35 U.S.C. 103 as being unpatentable over Goel et al. (Pub. No.: US 2016/0307098 A1), hereafter Goel, in view of Wang et al. (“Jumpout : Improved Dropout for Deep Neural Networks with ReLUs”), hereafter Wang, in further view of Li (“Understanding the Disharmony between Dropout and Batch Normalization by Variance Shift”), hereafter Li.
Regarding claim 1, Goel discloses:
An information processing method executed by a computer, the information processing method comprising (Goel, Figs. 2, 3, and 5, and ¶[0043]),
acquiring information indicating a dropout rate in training of a model (Goel, Fig. 4, element 402 and ¶[0053] teaches selecting an initial annealing schedule with a dropout rate, which is input into the system, as acquiring information indicating a dropout rate in training of a model),
the model comprising a first partial model and a second partial model (Goel, Fig. 1A, Fig. 1B, Fig. 4, ¶[0091] and ¶[0041] teaches the model to comprise first and second partial models as dropout is carried out iteratively to create different partial models at each iteration),
… a unit size of a hidden layer of the second partial model (Goel, Fig. 1B, Fig. 4 element 410 , ¶[0019], and ¶[0090] teaches adjusting the percentage of nodes to be dropped in model hidden layers, i.e. adjusting the unit size of the remaining layer in the model depicted in Fig. 1B) based on a function expressing a correlation between the dropout rate and the unit size of the hidden layer (Goel, ¶[0065] and ¶[0067] teaches the unit size to be based on functions (4), (6), and (7), where a correlation between dropout rate and a “probability distribution over… the number of active units in a layer of unit” is given),
wherein using the function reduces time for … the unit size as compared to … the unit size without using the function (Goel, ¶[0008] teaches the time for adjusting the unit size for regular dropout to be suboptimal compared to using the function),
training the model having the hidden layer with the … unit size; and… generating a trained model having the hidden layer with the … unit size (Goel, ¶[0054] teaches training and generating trained model(s) using the dropout training used to adjust unit size of layers),
wherein training the model comprises: performing forward propagation through the hidden layer (Goel, ¶[0066] teaches a forward pass of training as performing forward propagation through the hidden layer),
applying dropout to nodes of the hidden layer based on the dropout rate (Goel, ¶[0019] and [0066] teaches a applying dropout to nodes of the hidden layer based on the dropout rate),
updating weights through backpropagation (Goel, ¶[0067] and ¶[0077] teaches updating weights through back propagation),
wherein the trained model has improved generalization performance on unseen data as compared to a model trained without using the correlation (Goel, ¶[0058) teaches the trained model has improved generalization performance on unseen data during test time as compared to a model trained without using the correlation),
wherein the first partial model is trained by dropout based on a first dropout rate and the second partial model is trained by dropout based on a second dropout rate different from the first dropout rate (Goel, Fig. 1B, Fig. 4, and ¶[0091] teaches iteratively changing dropout rates for the successive partial models, i.e., first and second partial models).
While Goel teaches adjusting a unit size of a hidden layer based on a function expressing a correlation between the dropout rate and the unit size of the hidden layer, wherein using the function reduces time for … the unit size as compared to … the unit size without using the function, training the model having the hidden layer with the determined unit size; and generating a trained model having the hidden layer with the … unit size, they do not explicitly recite determining the unit size through the adjustment.
Wang discloses:
determining a unit size of a hidden layer (Wang, page 5, section 3.2 “Modification II: Dropout Rate adapted to the number of Activated Neurons”, paragraph 2, lines 8-16 “the fraction of active neurons in layer j is
PNG
media_image1.png
32
282
media_image1.png
Greyscale
… we normalize the dropout rate by qj+ and use an actual dropout rate of p’j = pj/qj+ ” explicitly teaches determining a unit size of a hidden layer by determining the active neurons in the layer),
training …model having the hidden layer with the determined unit size; and generating a trained model having the hidden layer with the determined unit size (Wang, Figure 2 and pages 4-5, section 3.2 teaches training and generating models having the determined unit size).
Goel and Wang are analogous art because they are from the same field of endeavor, dropout learning and neural network.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Goel to include determining the unit size, based on the teachings of Wang. One of ordinary skill in the art would have been motivated to make this modification in order “to better control the behavior of dropout for different layers and across various training stages”, as suggested by Wang (page 5, section 3.2, paragraph 2, lines 13-14).
While Goel discloses training the model, they do not disclose performing batch normalization after applying the dropout.
Li discloses:
performing batch normalization after applying the dropout (Li, Fig. 1, Fig. 2, and page 2, left column, penultimate paragraph, lines 2-6 “we adopted two strategies… to modify the formula of Dropout and made it less sensitive to variance.” Teaches performing batch normalization after their modified dropout).
Goel, Wang, and Li are analogous art because they are from the same field of endeavor, dropout and neural network.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Goel, in view of Wang, to include performing batch normalization after applying the dropout, based on the teachings of Li. One of ordinary skill in the art would have been motivated to make this modification in order to speed up all the modern architectures and also improve upon their strong baselines by acting as regularizers and avoid the variance shift risks, as suggested by Li (page 1, right column, first paragraph, lines 1-3 and page 2, left column, penultimate paragraph, line 6).
Regarding claim 2, Goel, in view of Wang, in further view of Li, discloses the information processing method according to claim 1. Goel further discloses:
generating the model including a hidden layer based on the dropout rate (Goel, Fig. 1A, Fig. 1B and ¶[0040-0042] teaches generating hidden nodes of hidden layers based on dropout rate during dropout training).
Regarding claim 3, Goel, in view of Wang, in further view of Li, discloses the information processing method according to claim 2. Goel further discloses:
generating the model including a hidden layer having a size determined based on the dropout rate (Goel, Fig. 1B and ¶[0042] teaches generating hidden nodes of hidden layers, i.e. the size of hidden layers, based on dropout rate during dropout training).
Regarding claim 4, Goel, in view of Wang, in further view of Li, discloses the information processing method according to claim 3. Goel further discloses:
generating the model including a hidden layer having a size determined based on a correlation between the dropout rate and the size of the hidden layer (Goel, Fig. 4, ¶[0005] and ¶[0024] teaches sizes of layers, i.e. number of layers/nodes, as network parameters and training to be based on a correlation between dropout rate and all parameters, which include the sizes of hidden layers).
Regarding claim 5, Goel, in view of Wang, in further view of Li, discloses the information processing method according to claim 4. Goel further discloses:
generating the model based on a positive correlation between the dropout rate and the size of the hidden layer (Goel, ¶[0022] and ¶[0067] teaches maximizing the performance of the model as generating the model based on a positive correlation between the dropout rate and the size of the hidden layer).
Regarding claim 6, Goel, in view of Wang, in further view of Li, discloses the information processing method according to claim 4. Goel further discloses:
generating the model including a hidden layer having a size determined using a function having the dropout rate and the size of the hidden layer as variables (Goel, Fig. 4, 408, and ¶[0065-0067] teaches a hidden layer having a size determined using a function having the dropout rate and the size of the hidden layer as variables).
Regarding claim 7, Goel, in view of Wang, in further view of Li, discloses the information processing method according to claim 6. Goel further discloses:
generating the model based on a target size specified based on the function, the target size being a size of the hidden layer corresponding to the dropout rate (Goel, Fig. 4, element 410-412 and ¶[0057] teaches generating the model based on a target size, i.e. fixed percentage of outputs, specified based on the function, the target size being a size of the hidden layer corresponding to the dropout rate).
Regarding claim 8, Goel, in view of Wang, in further view of Li, discloses the information processing method according to claim 7. Goel further discloses:
generating the model including a hidden layer having a size within a predetermined range from the target size (Goel, Fig. 4, element 410-412 and ¶[0057] teaches a hidden layer having a size within a predetermined range from the target size).
Regarding claim 9, Goel, in view of Wang, in further view of Li, discloses the information processing method according to claim 8. Goel further discloses:
generating the model including a hidden layer having a size with a highest accuracy among a plurality of sizes within a predetermined range from the target size (Goel, ¶[0020-0021] teaches a maximized generalization performance for a percentage of input/output neurons as generating the model including a hidden layer having a size with a highest accuracy, i.e. measurement of how well a learning machine generalizes to unseen (nontraining) data, among a plurality of sizes within a predetermined range from the target size).
Regarding claim 10, Goel, in view of Wang, in further view of Li, discloses the information processing method according to claim 9. Goel further discloses:
generating a plurality of models corresponding to a plurality of sizes within a predetermined range from the target size, respectively, are trained, and one model having a highest accuracy among the plurality of models as the model (Goel, ¶[0042] teaches training an ensemble of models as a plurality of models corresponding to a plurality of sizes within a predetermined range from the target size, to improve generalization performance).
Regarding claim 11, Goel, in view of Wang, in further view of Li, discloses the information processing method according to claim 1. Goel further discloses:
generating the model by performing batch normalization after dropout based on the dropout rate (Goel, ¶[0052] and ¶[0078] teaches dropouts per minibatches as performing batch normalization after dropout based on the dropout rate).
Regarding claim 12, Goel, in view of Wang, in further view of Li, discloses the information processing method according to claim 1. Goel further discloses:
the model includes an embedding layer in which an input is embedded (Goel, ¶[0040] hidden layers that transform inputs from input layers as embedding layer in which an input is embedded).
Regarding claim 13, Goel discloses:
An information processing apparatus comprising (Goel, Figs. 2, 3, and 5, and ¶[0043]),
an acquisition unit that acquires information indicating a dropout rate in training of a model (as per Claim interpretation of an acquisition unit that acquires information indicating a dropout rate in training of a model cited above: Goel, Fig. 4, element 402 and ¶[0053] teaches selecting an initial annealing schedule with a dropout rate, which is input into the system, as acquiring information indicating a dropout rate in training of a model),
the model comprising a first partial model and a second partial model (Goel, Fig. 1A, Fig. 1B, Fig. 4, ¶[0091] and ¶[0041] teaches the model to comprise first and second partial models as dropout is carried out iteratively to create different partial models at each iteration),
a determination unit that … a unit size of a hidden layer of the second partial model based on a function expressing a correlation between the dropout rate and the unit size of the hidden layer (as per Claim interpretation of a determination unit that determines a unit size cited above: Goel, Fig. 4 element 410 , ¶[0019], and ¶[0090] teaches adjusting the percentage of nodes to be dropped in model hidden layers, i.e. adjusting the unit size of the remaining layer in the model depicted in Fig. 1B and ¶[0065] and ¶[0067] teaches the unit size to be based on functions (4), (6), and (7), where a correlation between dropout rate and a “probability distribution over… the number of active units in a layer of unit” is given),
wherein using the function reduces time for … the unit size as compared to … the unit size without using the function (Goel, ¶[0008] teaches the time for adjusting the unit size for regular dropout to be suboptimal compared to using the function),
a generation unit that generates a trained model having the hidden layer with the … unit size by training the model having the hidden layer with the … unit size (as per Claim interpretation of a generation unit that generates a trained model cited above: Goel, Fig. 4, and ¶[0054] teaches training and generating trained model(s) using the dropout training used to adjust unit size of layers),
wherein training the model comprises: performing forward propagation through the hidden layer (Goel, ¶[0066] teaches a forward pass of training as performing forward propagation through the hidden layer),
applying dropout to nodes of the hidden layer based on the dropout rate (Goel, ¶[0019] and [0066] teaches a applying dropout to nodes of the hidden layer based on the dropout rate),
updating weights through backpropagation (Goel, ¶[0067] and ¶[0077] teaches updating weights through back propagation),
wherein the trained model has improved generalization performance on unseen data as compared to a model trained without using the correlation (Goel, ¶[0058) teaches the trained model has improved generalization performance on unseen data during test time as compared to a model trained without using the correlation),
wherein the first partial model is trained by dropout based on a first dropout rate and the second partial model is trained by dropout based on a second dropout rate different from the first dropout rate (Goel, Fig. 1B, Fig. 4, and ¶[0091] teaches iteratively changing dropout rates for the successive partial models, i.e., first and second partial models).
While Goel teaches adjusting a unit size of a hidden layer based on a function expressing a correlation between the dropout rate and the unit size of the hidden layer, wherein using the function reduces time for … the unit size as compared to … the unit size without using the function, and a generation unit that generates a trained model having the hidden layer with the … unit size by training the model having the hidden layer with the … unit size, they do not explicitly recite determining the unit size from this adjustment.
Wang discloses:
determining a unit size of a hidden layer (as per Claim interpretation of a determination unit that determines a unit size cited above: Wang, page 5, section 3.2 “Modification II: Dropout Rate adapted to the number of Activated Neurons”, paragraph 2, lines 8-16 “the fraction of active neurons in layer j is
PNG
media_image1.png
32
282
media_image1.png
Greyscale
… we normalize the dropout rate by qj+ and use an actual dropout rate of p’j = pj/qj+ ” explicitly teaches determining a unit size of a hidden layer by determining the active neurons in the layer),
generating a trained model having the hidden layer with the determined unit size by training …model having the hidden layer with the determined unit size (Wang, Figure 2 and pages 4-5, section 3.2 teaches generating and training models having the determined unit size).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Goel to include determining the unit size, based on the teachings of Wang. One of ordinary skill in the art would have been motivated to make this modification in order “to better control the behavior of dropout for different layers and across various training stages”, as suggested by Wang (page 5, section 3.2, paragraph 2, lines 13-14).
While Goel discloses training the model, they do not disclose performing batch normalization after applying the dropout.
Li discloses:
performing batch normalization after applying the dropout (Li, Fig. 1, Fig. 2, and page 2, left column, penultimate paragraph, lines 2-6 “we adopted two strategies… to modify the formula of Dropout and made it less sensitive to variance.” Teaches performing batch normalization after applying their modified dropout).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Goel, in view of Wang, to include performing batch normalization after applying the dropout, based on the teachings of Li. One of ordinary skill in the art would have been motivated to make this modification in order to speed up all the modern architectures and also improve upon their strong baselines by acting as regularizers and avoid the variance shift risks, as suggested by Li (page 1, right column, first paragraph, lines 1-3 and page 2, left column, penultimate paragraph, line 6).
Claim 14 is substantially similar to claim 1, and thus is rejected on the same basis as claim 1.
Response to Arguments
Applicant's arguments filed 05/14/2026 have been fully considered with regards to the 35 U.S.C. 102/103 rejection, but they are not persuasive.
The applicant asserts on page 7 of the remarks “Neither Goel nor Wang teaches or suggests a model comprising first and second partial models trained with different dropout rates”. The examiner respectfully disagrees, as Goel’s Fig. 1A, Fig. 1B, Fig. 4, ¶[0091] and ¶[0041] teaches the model to comprise first and second partial models as dropout is carried out iteratively to create different partial models at each iteration. See rejection above for further details.
The remainder of Applicant’s arguments with respect to claim 1 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claims substantially similar to claim 1 are rejected for the same reason. Claims dependent on independent claim 1 do not overcome the deficiencies of the rejected independent claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HUMAIRA ZAHIN MAUNI whose telephone number is (703)756-5654. The examiner can normally be reached Monday - Friday, 9 am - 5 pm (ET).
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, MATT ELL can be reached at (571) 270-3264. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/H.Z.M./Examiner, Art Unit 2141
/MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141
Prosecution Insights